A PREDICTIVE MODEL FOR QUERY OPTIMIZATION TECHNIQUES IN PERSONALIZED WEB SEARCH
|
|
- Veronica Banks
- 8 years ago
- Views:
Transcription
1 International Journal of Computer Science and System Analysis Vol. 5, No. 1, January-June 2011, pp Serials Publications ISSN A PREDICTIVE MODEL FOR QUERY OPTIMIZATION TECHNIQUES IN PERSONALIZED WEB SEARCH G. Pradeep and S. Prabakaran II Computer science and Engineering, Anna University, Trichirappalli, Tamilnadu, India spavcp@gmail.com Abstract: Personalized web search algorithms nowadays used are not effective on different queries for different users and under different search contexts. In this Project. A large-scale evaluation framework for personalized search based on query logs were built and then it is decided to evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, it was concluded that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. It is decided to propose the click entropy as a simple measurement on whether a query should be personalized. It is decided to propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results will show that using a personalization algorithm for queries selected this prediction model is better than using it simply for all queries. Re ranking of user visited pages are automatically done by the user s click actions. Keywords: Web search, Reranking factors, User actions, Click entropy, query optimization. I. INTRODUCTION As the amount of information [24] on the Web rapidly increases, it creates many new challenges for Web search. When the same query is submitted by different users, a typical search engine returns the same result, regardless of who submitted the query. This may not be suitable for users with different information needs. For example, for the query apple, some users may be interested in documents dealing with apple as fruit, while some other users may want documents related to Apple computers. One way to disambiguate the words in a query is to associate a small set of categories with the query. For example, if the category cooking or the category fruit is associated with the query apple, then the user s intention becomes clear. Current search engines such as Goggle or Yahoo! have hierarchies of categories to help users to specify their intentions. The use of hierarchical categories such as the Library of Congress Classification is also common among librarians. One criticism of search engines is that when queries are issued, most return the same results to users. In fact, the vast majority of queries to search engines are short and ambiguous. Different users may have completely different information needs and goals when using precisely the same query. Personalized web search is considered a solution to address these problems, since it can provide different search results based upon the preference of users. A measure of a query with respect to a collection of documents with the aim of quantifying the query s ambiguity with respect to those documents was developed by (Steve Cronin Townsend S, 2002). This measure, the clarity score, is the relative entropy between a query language model and the corresponding collection language model. The clarity score measures the coherence and specificity of the language used in documents likely to satisfy the query. It was argued that it provides a suitable quantification of the lack of ambiguity of a query with respect to a collection of documents and has potential applications throughout the information retrieval. In particular, the clarity score is shown to correlate positively with average precision in evaluations using TREC test collections. Hence, as one example, the clarity score could serve as a predictor of query performance. Systems would then be able to identify vague information requests and respond differently than they would to clear and specific requests. Re ranking algorithm is used to re rank the user visited URL pages based on the user actions in the
2 38 International Journal of Computer Sciences and System Analysis prescribed web pages. User clicks are classified based on the actions which may be save, bookmarks etc., This effective procedure will automatically increase the overall performance of the Personalized Web Searches. II. RELATED WORK Current web search engines [5] are built to serve all users, independent of the needs of any individual user. Personalization of web search is to carry out retrieval for each user incorporating his/her interests. A novel technique to map a user query to a set of categories, which represent the user s search intention. was to be adopted. This set of categories can serve as a context to disambiguate the words in the user s query. A user profile and a general profile are learned from the user s search history and a category hierarchy respectively. These two profiles are combined to map a user query into a set of categories. Several learning and combining algorithms are evaluated and found to be effective.among the algorithms to learn a user profile, we choose the Rocchio-based method for its simplicity, efficiency and its ability to be adaptive. Experimental results indicate that our technique to personalize web search is both effective and efficient. Web search engines help users find useful information on the World Wide Web (WWW). However, [23] when different users submit the same query, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search results should be adapted to users with different information needs. Experimental results show that [23] search systems that adapt to each user s preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user s browsing history in one day. One hundred users, one hundred needs. As more and more topics are being discussed on the web [6] and our vocabulary remains relatively stable, it is increasingly difficult to let the search engine know what we want. Coping with ambiguous queries has long been an important part of the research on Information Retrieval, but still remains a challenging task. Personalized search has recently got significant attention in addressing this challenge in the web search community, based on the premise that a user s general preference may help the search engine disambiguate the true intention of a query. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. A study was made to know how a search engine can learn a user s preference automatically based on her past click history and how it can use the user preference to personalize search results. The experiments show that users preferences can be learned accurately even from little click-history data and personalized search based on user preference yields significant improvements over the best existing ranking mechanism in the literature. The Web is a highly [10] distributed and heterogeneous information environment. The immense number of documents on the Web produces various challenges for search engines. Storage space, crawling speed, computational speed and retrieval of most relevant documents are some examples of these challenges. In this picture, it is important to define the relevancy of the documents as most popular and best quality documents. When ranking the html pages, you may judge about the quality of a page: by analyzing its content, by measuring its popularity or by examining its connectivity. The Information retrieval systems [5] (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word java to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user s interest from the user s search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine. Long-term search history [10] contains rich information about a user s Search preferences. A study was made regarding statistical language modeling based methods to mine contextual information from long-term search history and to exploit it for more
3 A Predictive Model for Query Optimization Techniques in Personalized Web Search 39 accurate estimates of the query model. The experiments on a web search test collection show that the algorithms are effective in improving retrieval accuracy for both fresh and recurring queries. The best performance is achieved when using the combination of related past searches and Clickthrough data as the main source of search context. The PC Desktop [25] is a very rich repository of personal information, efficiently capturing user s interests. It is proposed to have a new approach towards an automatic personalization of web search in which the user specific information is extracted from such local desktops, thus allowing for an increased quality of user profiling, while sharing less private information with the search engine. More Specifically, we investigate the opportunities to select personalized query expansion terms for web search using three different desktop oriented approaches: summarizing the entire desktop data, summarizing only the desktop documents relevant to each user query, and applying natural language processing techniques to extract dispersive lexical compounds from relevant desktop resources. The experiments with the Google API showed at least the latter two techniques to produce a very strong improvement over current web search. A method for predicting query performance by computing the relative entropy between a query language model and the corresponding collection language model was developed. The resulting clarity score measures the coherence of the language usage in documents whose models are likely to generate the query. We suggest that clarity scores measure the ambiguity of a query with respect to a collection of documents and show that they correlate positively with average precision in a variety of TREC test sets. Thus, the clarity score may be used to identify ineffective queries, on average, without relevance information. An algorithm for automatically setting the clarity score threshold between predicted poorly performing queries and acceptable queries and validates it using TREC data was developed. A Comparison was made for the automatic thresholds to optimum thresholds and also check how frequently results as good are achieved in sampling experiments that randomly assign queries to the two classes. III. EXPERIMENTAL METHODOLOGY To evaluate the performance of personalized search, each participant is required to issue a certain number of test queries and determine whether each result is relevant. An advantage of this approach is that the relevance of documents can be explicitly judged by the participants. Unfortunately, there are some drawbacks in this method. Constraints on the number of participants and test queries may bias evaluation results on accuracy and reliability of the personalization algorithm Query Optimization in Personalized Web Search As the amount of information on the Web rapidly increases, it creates many new challenges for Web search. When the same query is submitted by different users, a typical search engine returns the same result, regardless of who submitted the query. This may not be suitable for users with different information needs. For example, for the query apple, some users may be interested in documents dealing with apple as fruit, while some other users may want documents related to Apple computers. One way to disambiguate the words in a query is to associate a small set of categories with the query. For example, if the category cooking or the category fruit is associated with the query apple, then the user s intention becomes clear. Current search engines such as Goggle or Yahoo! have hierarchies of categories to help users to specify their intentions. The use of hierarchical categories such as the Library of Congress Classification is also common among librarians. One criticism of search engines is that when queries are issued, most return the same results to users. In fact, the vast majority of queries to search engines are short and ambiguous. Different users may have completely different information needs and goals when using precisely the same query. Personalized web search is considered a solution to address these problems, since it can provide different search results based upon the preference of users. A re ranking evaluation frame work is to be constructed by first downloading the search results from the windows live search engine. Then, by using the selected Personalization algorithm to re rank search results. Operation Steps in the Proposed system : 1. Download the top 50 search results from the search engine for the query string. 2. Compute a Personalized score for each item using a Personalization Algorithm and generate
4 40 International Journal of Computer Sciences and System Analysis a rank list result items are to be sorted in descending order based on the personalised scores. 3. Combine the two rank lists and generate the final rank list, which will be returned to the users in personalized search. 4. The ranks of Clicked URL are in a log entry and use the events to evaluate the performance of the query Features Used To Predict Query Performance (1) Click Entropy Click Entropy is a direct indication of query click variation. If all users click only one identical page on query, Click Entropy (q) = 0. A Smaller click entropy means that the majority of users agree with each other on a small number of web pages. In such cases, there is no need to do any personalization. A Large click entropy indicates that many web pages were clicked for the query. This mean the following: A user has to select several pages to satisfy his information need, which means the query is most likely an informational query. Different users have different selections on this query, which means that the query is an ambiguous query. (2) Click Diversity The goal of personalized web search is to return different results to different users according to their preferences. A direct way to identify whether users have different preferences on a query is to check the click diversity of users. Click entropy is one of such measures of click diversity. For a given query, suppose there are K users who ever issued this query, and there are M documents that are clicked for this query. Then click frequency was calculated for each user on each document and represent them in a K X M user document matrix X. Each element x (k,m) =c, indicates that user k clicked document m by c times. If the user has not clicked the document, then x (k,m) =0. (3) Concept Diversity This is another way to identify the diversity of user preferences over a query to measure the concept / topic diversity of clicked documents. Each document can be classified into one or more concept / topic categories. A document concept matrix is used to represent categories of documents. (4) ExRatio Obviously, users usually reformulate ambiguous queries. A common reformulation is adding terms to the original query. So, we extract feature ExRatio based on this information. Num of sessions is the Number of sessions that the query appears. Num of sessions Ex is the Number of session that the query appears and at least one extended query also appears. The Ex Ratio is calculated by, Ex Ratio = Num of sessions Ex / Num of sessions (5) Isfirstqueryinsession If a query is the first query of a session, S-Topic can t work for it. (6) Hasqueryhistory This feature indicates whether the query has been issued in the past.. (7) Avgclktimes This feature displays the average historical click times per query forthe query string. If users usually click multiple results for a query, this query is more likely to be an ambiguous or informational query Personalization Algorithms The personalization algorithms are used to rerank search results by computing a personalized score for each document for the results returned by each user query. Two strategies are to be implemented as Person- Level Re ranking Historical Click Based Algorithm A query submitted by a user, the web pages frequently clicked by the user in the past are more relevant to the user than those seldom clicked by the user. Based on this a personalized score on page can be computed. A disadvantage of this approach is that it is not applicable for new queries that the user has never asked. If the data set contains 1/3 of the same user
5 A Predictive Model for Query Optimization Techniques in Personalized Web Search 41 issues the test queries more than one time. This approach would only benefit these queries User-Topical Interest Based Algorithm A Personalization method based on long-term user topical interests are represented as a vector. When a user submits a query, each returned web page is first mapped to a category vector. Then the similarity between the user profile vector and the page category vector is computed. Table 1 Basic Statistics of Data Set Item All Training Test #days #users 10,000 10,000 1,792 #queries 55,937 51,334 4,639 #distinct queries 34,203 31,777 3,465 #clicks 93,566 85,642 7,924 #clicks/#queries #sessions 49,839 45,981 3, Group Level Re Ranking Implementation of K- Nearest Neighbor CF algorithm as a representative of a group based personalization. Computations on user similarity based on long term user profiles Performance Of Proposed Algorithms the same user. It shows that user often resubmit a query and review the results they have searched. Repeated clicks can be predicted based upon a user s historical queries and clicks. Table 3 Performanace of Repeated Queries Method All queries Non-optimal queries Repeated Not-rep. Repeated Non-rep. WEB P-Click L-Topic S-Topic LS-Topic G-Click Table 4 Performance of Self Repeated Queries Method All queries Non-optimal queries Self-repeated Not-rep. Self-repeated Not-rep. WEB P-Click L-Topic S-Topic LS-Topic G-Click Table 2 Overall Performance of Strategies Method All queries Non-optimal Optimal queries queries WEB P-Click % % % L-Topic % % % S-Topic % % % LS-Topic % % % G-Click % % % The above Table 2 Shows that Topical-interestbased strategies perform less well than Click based strategies and the baseline Performance on Repeated Queries In this frame work, 46 per cent of test queries are once repeated, and 33 per cent of queries are repeated by Figure 1: Performance of Queries Based on user Actions The above figure (Fig 1) shows the performance analysis of queries based on user actions. 4. CONCLUSIONS The algorithms stated in this framework only for repeated queries, but they are stable and simple. The topical- interest based personalized search algorithms implemented were not as stable as the click based ones under this framework. They could improve search
6 42 International Journal of Computer Sciences and System Analysis accuracy for some queries, but they harmed performance for more queries. Another important conclusion regarding this framework is that personalization does not work equally well under various situations. Results show that personalized web search yields significant improvements over generic web search for queries with high click entropy. All the queries should not be handled in the same manner. No Personalization algorithm can outperform others for all queries. Different methods have different strengths and weaknesses. The main objective of optimizing the query thereby increasing the effectiveness of personalized web search is achieved. To enhance further, promising direction can be explored in the future is to automatically predict which algorithm should be used for a given query and to combine the strength of different personalization methods. References [1] C. Silverstein, H. Marais, M. Henzinger, and M. Moricz, Analysis of a Very Large Web Search Engine Query Log, ACM SIGIR Forum, 33(1), 6-12, [2] B. J. Jansen, A. Spink, and T. Saracevic, Real Life, Real Users, and Real Needs: A Study and Analysis of User Queries on the Web, Information Processing and Management, 36(2), , [3] R. Krovetz and W. B. Croft, Lexical Ambiguity and Information Retrieval, Information Systems, 10(2), , [4] S. Cronen-Townsend and W. B. Croft, Quantifying Query Ambiguity, Proc. Second Int l Conf. Human Language Technology Research (HLT 02), pp , [5] X. Shen, B. Tan, and C. Zhai, Implicit User Modeling for Personalized Search, Proc. ACM Int l Conf. Information and Knowledge Management (CIKM 05), pp , [6] F. Qiu and J. Cho, Automatic Identification of User Interest for Personalized Search, Proc. 15th Int l World Wide Web Conf. (WWW 06), pp , [7] J. Teevan, S. T. Dumais, and E. Horvitz, Beyond the Commons: Investigating the Value of Personalizing Web Search, Proc. Workshop New Technologies for Personalized Information Access (PIA), [8] J. Pitkow, H. Schutze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel, Personalized Search, Comm. ACM, 45(9), 50-55, [9] A. Pretschner and S. Gauch, Ontology Based Personalized Search, Proc. 11th IEEE Int l Conf. Tools with Artificial Intelligence (ICTAI 99), pp , [10] B. Tan, X. Shen, and C. Zhai, Mining Long-Term Search History to Improve Search Accuracy, Proc. 12fth ACM SIGKDD Int l Conf. Knowledge Discovery and Data Mining (KDD 06), , [11] G. Jeh and J. Widom, Scaling Personalized Web Search, Proc. 12th Int l World Wide Web Conf. (WWW 03), , [12] P. Ferragina and A. Gulli, A Personalized Search Engine Based on Web-Snippet Hierarchical Clustering, Special Interest Tracks and Posters of the 14th Int l Conf. World Wide Web (WWW 05), , [13] J. Teevan, S. T. Dumais, and E. Horvitz, Personalizing Search via Automated Analysis of Interests and Activities, Proc. 28th Ann. Int l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 05), , [14] J. T. Sun, H. J. Zeng, H. Liu, Y. Lu, and Z. Chen, CubeSVD: A Novel Approach to Personalized Web Search, Proc. 14th Int l World Wide Web Conf. (WWW 05), , [15] F. Liu, C. Yu, and W. Meng, Personalized Web Search by Mapping User Queries to Categories, Proc. ACM Int l Conf. Information and Knowledge Management (CIKM 02), , [16] P. A. Chirita, W. Nejdl, R. Paiu, and C. Kohlschu tter, Using ODP Metadata to Personalize Search, Proc. 28th Ann. Int l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 05), , [17] A. Broder, A Taxonomy of Web Search, ACM SIGIR Forum, 36(2), 3-10, [18] U. Lee, Z. Liu, and J. Cho, Automatic Identification of User Goals in Web Search, Proc. 14th Int l World Wide Web Conf. (WWW 05), , [19] X. Shen, B. Tan, and C. Zhai, Context-Sensitive Information Retrieval Using Implicit Feedback, Proc. 28th Ann. Int l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 05), 43-50, [20] Windows Live Search, [21] J. R. Wen, Z. Dou, and R. Song, Personalized Web Search, Encyclopedia of Database Systems, [22] J. M. Carroll and M.B. Rosson, Paradox of the Active User, Interfacing Thought: Cognitive Aspects of Human-Computer Interaction, , [23] K. Sugiyama, K. Hatano, and M. Yoshikawa, Adaptive WebSearch Based on User Profile Constructed without Any Effort from Users, Proc. 13th Int l World Wide Web Conf. (WWW 04), , [24] F. Liu, C. Yu, and W. Meng, Personalized Web Search for Improving Retrieval Effectiveness, IEEE Trans. Knowledge and Data Eng., 16(1), 28-40, [25] P. A. Chirita, C. Firan, and W. Nejdl, Summarizing Local Context to Personalize Global Web Search, Proc. ACM Int l Conf. Information and Knowledge Management (CIKM), [26] J. Chaffee and S. Gauch, Personal Ontologies for Web Navigation, Proc. ACM Int l Conf. Information and Knowledge Management (CIKM 00), , [27] S. Gauch, J. Chaffee, and A. Pretschner, Ontology-Based Personalized Search and Browsing, Web Intelligence and Agent Systems, 1(3/4), , 2003.
7 A Predictive Model for Query Optimization Techniques in Personalized Web Search 43 [28] J. Trajkova and S. Gauch, Improving Ontology-Based User Profiles, Proc. Recherche d Information Assiste e par Ordinateur (RIAO 04), , [29] M. Speretta and S. Gauch, Personalized Search Based on User Search Histories, Proc. IEEE/WIC/ACM Int l Conf. Web Intelligence (WI 05), , [30] L. Page, S. Brin, R. Motwani, and T. Winograd, The PageRank Citation Ranking: Bringing Order to the Web, technical report, Computer Science Dept., Stanford Univ., [31] T. H. Haveliwala, Topic-Sensitive Pagerank, Proc. 11th Int l World Wide Web Conf. (WWW), [32] T. Sarlo s, A.A. Benczu r, K. Csaloga ny, D. Fogaras, and B. Ra cz, To Randomize or Not to Randomize: Space Optimal Summaries for Hyperlink Analysis, Proc. 15th Int l World Wide Web Conf. (WWW 06), , [33] F. Tanudjaja and L. Mui, Persona: A Contextualized and Personalized Web Search, Proc. 35th Hawaii Int l Conf. System Sciences (HICSS 02), 3, 53, [34] J. S. Breese, D. Heckerman, and C. Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI 98), 43-52, [35] P. A. Chirita, C. S. Firan, and W. Nejdl, Personalized Query Expansion for the Web, Proc. 30th Ann. Int l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 07), 7-14, [36] J. Teevan, S. T. Dumais, and D. J. Liebling, To Personalize or Not to Personalize: Modeling Queries with Variation in User Intent, Proc. 31th Ann. Int l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 08), [37] S. Cronen-Townsend, Y. Zhou, and W. B. Croft, Predicting Query Performance, Proc. 25th Ann. Int l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 02), , 2002.
International Journal of Engineering Research-Online A Peer Reviewed International Journal Articles available online http://www.ijoer.
REVIEW ARTICLE ISSN: 2321-7758 UPS EFFICIENT SEARCH ENGINE BASED ON WEB-SNIPPET HIERARCHICAL CLUSTERING MS.MANISHA DESHMUKH, PROF. UMESH KULKARNI Department of Computer Engineering, ARMIET, Department
More informationA Large scale Evaluation and Analysis of Personalized Search Strategies
A Large scale Evaluation and Analysis of Personalized Search Strategies Zhicheng Dou Nankai University Tianjin 30007, China douzc@yahoo.com.cn Ruihua Song Microsoft Research Asia Beijing 00080, China rsong@microsoft.com
More informationLDA Based Security in Personalized Web Search
LDA Based Security in Personalized Web Search R. Dhivya 1 / PG Scholar, B. Vinodhini 2 /Assistant Professor, S. Karthik 3 /Prof & Dean Department of Computer Science & Engineering SNS College of Technology
More informationISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies Research Article / Paper / Case Study Available online at: www.ijarcsms.com
More informationSupporting Privacy Protection in Personalized Web Search
Supporting Privacy Protection in Personalized Web Search Kamatam Amala P.G. Scholar (M. Tech), Department of CSE, Srinivasa Institute of Technology & Sciences, Ukkayapalli, Kadapa, Andhra Pradesh. ABSTRACT:
More informationPersonalization of Web Search With Protected Privacy
Personalization of Web Search With Protected Privacy S.S DIVYA, R.RUBINI,P.EZHIL Final year, Information Technology,KarpagaVinayaga College Engineering and Technology, Kanchipuram [D.t] Final year, Information
More informationSECURED AN IMPLEMENTATION OF PERSONALIZED WEB SEARCH
SECURED AN IMPLEMENTATION OF PERSONALIZED WEB SEARCH 1 Mr. A. MANIKANDAN, 2 Dr. A.VIJAYA KATHIRAVAN 1 Research Scholar, 2 Assistant Professor in Computer Science, 1 Dept of Computer Science, 2 Dept of
More informationHow To Cluster On A Search Engine
Volume 2, Issue 2, February 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: A REVIEW ON QUERY CLUSTERING
More informationProfile Based Personalized Web Search and Download Blocker
Profile Based Personalized Web Search and Download Blocker 1 K.Sheeba, 2 G.Kalaiarasi Dhanalakshmi Srinivasan College of Engineering and Technology, Mamallapuram, Chennai, Tamil nadu, India Email: 1 sheebaoec@gmail.com,
More informationA UPS Framework for Providing Privacy Protection in Personalized Web Search
A UPS Framework for Providing Privacy Protection in Personalized Web Search V. Sai kumar 1, P.N.V.S. Pavan Kumar 2 PG Scholar, Dept. of CSE, G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh,
More informationSustaining Privacy Protection in Personalized Web Search with Temporal Behavior
Sustaining Privacy Protection in Personalized Web Search with Temporal Behavior N.Jagatheshwaran 1 R.Menaka 2 1 Final B.Tech (IT), jagatheshwaran.n@gmail.com, Velalar College of Engineering and Technology,
More informationDynamical Clustering of Personalized Web Search Results
Dynamical Clustering of Personalized Web Search Results Xuehua Shen CS Dept, UIUC xshen@cs.uiuc.edu Hong Cheng CS Dept, UIUC hcheng3@uiuc.edu Abstract Most current search engines present the user a ranked
More informationPersonalizing Web Search using Long Term Browsing History
Personalizing Web Search using Long Term Browsing History ABSTRACT Nicolaas Matthijs University of Cambridge 15 JJ Thomson Avenue Cambridge, UK nm417@cam.ac.uk Personalizing web search results has long
More informationPrivacy Protection in Personalized Web Search- A Survey
Privacy Protection in Personalized Web Search- A Survey Greeshma A S. * Lekshmy P. L. M.Tech Student Assistant Professor Dept. of CSE & Kerala University Dept. of CSE & Kerala University Thiruvananthapuram
More informationSemantic Search in Portals using Ontologies
Semantic Search in Portals using Ontologies Wallace Anacleto Pinheiro Ana Maria de C. Moura Military Institute of Engineering - IME/RJ Department of Computer Engineering - Rio de Janeiro - Brazil [awallace,anamoura]@de9.ime.eb.br
More informationA Novel Framework for Personalized Web Search
A Novel Framework for Personalized Web Search Aditi Sharan a, * Mayank Saini a a School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi-67, India Abstract One hundred users, one
More informationData Mining in Web Search Engine Optimization and User Assisted Rank Results
Data Mining in Web Search Engine Optimization and User Assisted Rank Results Minky Jindal Institute of Technology and Management Gurgaon 122017, Haryana, India Nisha kharb Institute of Technology and Management
More informationPrivacy-Enhancing Personalized Web Search
Privacy-Enhancing Personalized Web Search Yabo Xu* Simon Fraser University 8888 University Drive, Burnaby BC, Canada yxu@cs.sfu.ca Benyu Zhang, Zheng Chen Microsoft Research Asia 5F, Beijing Sigma Center
More informationLink Analysis and Site Structure in Information Retrieval
Link Analysis and Site Structure in Information Retrieval Thomas Mandl Information Science Universität Hildesheim Marienburger Platz 22 31141 Hildesheim - Germany mandl@uni-hildesheim.de Abstract: Link
More informationRANKING WEB PAGES RELEVANT TO SEARCH KEYWORDS
ISBN: 978-972-8924-93-5 2009 IADIS RANKING WEB PAGES RELEVANT TO SEARCH KEYWORDS Ben Choi & Sumit Tyagi Computer Science, Louisiana Tech University, USA ABSTRACT In this paper we propose new methods for
More informationOptimized Mobile Search Engine
Optimized Mobile Search Engine E.Chaitanya 1, Dr.Sai Satyanarayana Reddy 2, O.Srinivasa Reddy 3 1 M.Tech, CSE, LBRCE, Mylavaram, 2 Professor, CSE, LBRCE, Mylavaram, India, 3 Asst.professor,CSE,LBRCE,Mylavaram,India.
More informationA SURVEY OF PERSONALIZED WEB SEARCH USING BROWSING HISTORY AND DOMAIN KNOWLEDGE
A SURVEY OF PERSONALIZED WEB SEARCH USING BROWSING HISTORY AND DOMAIN KNOWLEDGE GOODUBAIGARI AMRULLA 1, R V S ANIL KUMAR 2,SHAIK RAHEEM JANI 3,ABDUL AHAD AFROZ 4 1 AP, Department of CSE, Farah Institute
More informationIdentifying Best Bet Web Search Results by Mining Past User Behavior
Identifying Best Bet Web Search Results by Mining Past User Behavior Eugene Agichtein Microsoft Research Redmond, WA, USA eugeneag@microsoft.com Zijian Zheng Microsoft Corporation Redmond, WA, USA zijianz@microsoft.com
More informationSubordinating to the Majority: Factoid Question Answering over CQA Sites
Journal of Computational Information Systems 9: 16 (2013) 6409 6416 Available at http://www.jofcis.com Subordinating to the Majority: Factoid Question Answering over CQA Sites Xin LIAN, Xiaojie YUAN, Haiwei
More informationRank Optimization of Personalized Search
DEIM Forum 2009 A9-5 Ran Optimization of Personalized Search Lin LI, Zhenglu YANG, and Masaru KITSUREGAWA Dept. of Info. and Comm. Engineering, University of Toyo, 4-6-1 Komaba, Meguro-u, Toyo, 153-8305
More informationInterest-Based Personalized Search
Interest-Based Personalized Search ZHONGMING MA, GAUTAM PANT, and OLIVIA R. LIU SHENG The University of Utah Web search engines typically provide search results without considering user interests or context.
More informationImproving Web Page Retrieval using Search Context from Clicked Domain Names
Improving Web Page Retrieval using Search Context from Clicked Domain Names Rongmei Li School of Electrical, Mathematics, and Computer Science University of Twente P.O.Box 217, 7500 AE, Enschede, the Netherlands
More informationOptimization of Search Results with Duplicate Page Elimination using Usage Data A. K. Sharma 1, Neelam Duhan 2 1, 2
Optimization of Search Results with Duplicate Page Elimination using Usage Data A. K. Sharma 1, Neelam Duhan 2 1, 2 Department of Computer Engineering, YMCA University of Science & Technology, Faridabad,
More informationPrivacy Protection in Personalized Web Search Via Taxonomy Structure
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 54-65 www.iosrjournals.org Privacy Protection in Personalized Web Search
More informationOptimization of Image Search from Photo Sharing Websites Using Personal Data
Optimization of Image Search from Photo Sharing Websites Using Personal Data Mr. Naeem Naik Walchand Institute of Technology, Solapur, India Abstract The present research aims at optimizing the image search
More informationAmerican Journal of Engineering Research (AJER) 2013 American Journal of Engineering Research (AJER) e-issn: 2320-0847 p-issn : 2320-0936 Volume-2, Issue-4, pp-39-43 www.ajer.us Research Paper Open Access
More informationMALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph
MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph Janani K 1, Narmatha S 2 Assistant Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of
More informationSUSTAINING PRIVACY PROTECTION IN PERSONALIZED WEB SEARCH WITH TEMPORAL BEHAVIOR
International Journal of Latest Research in Science and Technology Volume 4, Issue 5: Page No.73-77, September-October 2015 http://www.mnkjournals.com/ijlrst.htm ISSN (Online):2278-5299 SUSTAINING PRIVACY
More informationAchieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services
Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services Ms. M. Subha #1, Mr. K. Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional
More informationPersonalized Image Search for Photo Sharing Website
Personalized Image Search for Photo Sharing Website Asst. Professor Rajni Pamnani rajaniaswani@somaiya.edu Manan Desai manan.desai@somaiya.edu Varshish Bhanushali varshish.b@somaiya.edu Ashish Charla ashish.charla@somaiya.edu
More informationPerformance evaluation of Web Information Retrieval Systems and its application to e-business
Performance evaluation of Web Information Retrieval Systems and its application to e-business Fidel Cacheda, Angel Viña Departament of Information and Comunications Technologies Facultad de Informática,
More informationMining Signatures in Healthcare Data Based on Event Sequences and its Applications
Mining Signatures in Healthcare Data Based on Event Sequences and its Applications Siddhanth Gokarapu 1, J. Laxmi Narayana 2 1 Student, Computer Science & Engineering-Department, JNTU Hyderabad India 1
More informationLearning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search
Feature Article: Ahu Sieg, Bamshad Mobasher and Robin Burke 7 Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search Ahu Sieg, Bamshad Mobasher, Robin Burke Center for Web
More informationA SECURE FRAMEWORK FOR PROTECTING IN PERSONALIZED WEB SEARCH Mrs. M.Sowjanya 1, Dr. P. Harini 2
A SECURE FRAMEWORK FOR PROTECTING IN PERSONALIZED WEB SEARCH Mrs. M.Sowjanya 1, Dr. P. Harini 2 1 II M.Tech. - II Sem., Dept. of CSE, St. Ann s College of Engineering. & Technology. Chirala, Andhra Pradesh
More informationAn Approach to Give First Rank for Website and Webpage Through SEO
International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-2 Issue-6 E-ISSN: 2347-2693 An Approach to Give First Rank for Website and Webpage Through SEO Rajneesh Shrivastva
More informationSemantic Concept Based Retrieval of Software Bug Report with Feedback
Semantic Concept Based Retrieval of Software Bug Report with Feedback Tao Zhang, Byungjeong Lee, Hanjoon Kim, Jaeho Lee, Sooyong Kang, and Ilhoon Shin Abstract Mining software bugs provides a way to develop
More informationEfficient Query Optimizing System for Searching Using Data Mining Technique
Vol.1, Issue.2, pp-347-351 ISSN: 2249-6645 Efficient Query Optimizing System for Searching Using Data Mining Technique Velmurugan.N Vijayaraj.A Assistant Professor, Department of MCA, Associate Professor,
More informationComparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search
Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search Orland Hoeber and Hanze Liu Department of Computer Science, Memorial University St. John s, NL, Canada A1B 3X5
More informationWeb Search Personalization Based on Browsing History by Artificial Immune System
Int. J. Advance. Soft Comput. Appl., Vol. 2, No. 3, November 2010 ISSN 2074-8523; Copyright ICSRS Publication, 2010 www.i-csrs.org Web Search Personalization Based on Browsing History by Artificial Immune
More informationPredicting User Interests from Contextual Information
Predicting User Interests from Contextual Information Ryen W. White Microsoft Research Redmond, WA 98052 ryenw@microsoft.com Peter Bailey Microsoft Redmond, WA 98052 pbailey@microsoft.com Liwei Chen Microsoft
More informationMapping User Search Queries to Product Categories
Mapping User Search Queries to Product Categories Carolyn T. Hafernik, Bin Cheng, Paul Francis, and Bernard J. Jansen College of Information Sciences and Technology, The Pennsylvania State University,
More informationFiltering Noisy Contents in Online Social Network by using Rule Based Filtering System
Filtering Noisy Contents in Online Social Network by using Rule Based Filtering System Bala Kumari P 1, Bercelin Rose Mary W 2 and Devi Mareeswari M 3 1, 2, 3 M.TECH / IT, Dr.Sivanthi Aditanar College
More informationSemantically Enhanced Web Personalization Approaches and Techniques
Semantically Enhanced Web Personalization Approaches and Techniques Dario Vuljani, Lidia Rovan, Mirta Baranovi Faculty of Electrical Engineering and Computing, University of Zagreb Unska 3, HR-10000 Zagreb,
More informationHow To Write A Summary Of A Review
PRODUCT REVIEW RANKING SUMMARIZATION N.P.Vadivukkarasi, Research Scholar, Department of Computer Science, Kongu Arts and Science College, Erode. Dr. B. Jayanthi M.C.A., M.Phil., Ph.D., Associate Professor,
More informationA Comparative Study on Sentiment Classification and Ranking on Product Reviews
A Comparative Study on Sentiment Classification and Ranking on Product Reviews C.EMELDA Research Scholar, PG and Research Department of Computer Science, Nehru Memorial College, Putthanampatti, Bharathidasan
More informationExtending a Web Browser with Client-Side Mining
Extending a Web Browser with Client-Side Mining Hongjun Lu, Qiong Luo, Yeuk Kiu Shun Hong Kong University of Science and Technology Department of Computer Science Clear Water Bay, Kowloon Hong Kong, China
More informationBisecting K-Means for Clustering Web Log data
Bisecting K-Means for Clustering Web Log data Ruchika R. Patil Department of Computer Technology YCCE Nagpur, India Amreen Khan Department of Computer Technology YCCE Nagpur, India ABSTRACT Web usage mining
More informationBlog Post Extraction Using Title Finding
Blog Post Extraction Using Title Finding Linhai Song 1, 2, Xueqi Cheng 1, Yan Guo 1, Bo Wu 1, 2, Yu Wang 1, 2 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 2 Graduate School
More informationImplementation of P2P Reputation Management Using Distributed Identities and Decentralized Recommendation Chains
Implementation of P2P Reputation Management Using Distributed Identities and Decentralized Recommendation Chains P.Satheesh Associate professor Dept of Computer Science and Engineering MVGR college of
More informationConventional. Personalized
Volume 3, Issue 8, August 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Personalized
More informationIncorporating Participant Reputation in Community-driven Question Answering Systems
Incorporating Participant Reputation in Community-driven Question Answering Systems Liangjie Hong, Zaihan Yang and Brian D. Davison Department of Computer Science and Engineering Lehigh University, Bethlehem,
More informationSEO Techniques for various Applications - A Comparative Analyses and Evaluation
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727 PP 20-24 www.iosrjournals.org SEO Techniques for various Applications - A Comparative Analyses and Evaluation Sandhya
More informationIntinno: A Web Integrated Digital Library and Learning Content Management System
Intinno: A Web Integrated Digital Library and Learning Content Management System Synopsis of the Thesis to be submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master
More informationInternational Journal of Engineering Research-Online A Peer Reviewed International Journal Articles are freely available online:http://www.ijoer.
RESEARCH ARTICLE SURVEY ON PAGERANK ALGORITHMS USING WEB-LINK STRUCTURE SOWMYA.M 1, V.S.SREELAXMI 2, MUNESHWARA M.S 3, ANIL G.N 4 Department of CSE, BMS Institute of Technology, Avalahalli, Yelahanka,
More informationClient Perspective Based Documentation Related Over Query Outcomes from Numerous Web Databases
Beyond Limits...Volume: 2 Issue: 2 International Journal Of Advance Innovations, Thoughts & Ideas Client Perspective Based Documentation Related Over Query Outcomes from Numerous Web Databases B. Santhosh
More informationMetasearch Engines. Synonyms Federated search engine
etasearch Engines WEIYI ENG Department of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USA Synonyms Federated search engine Definition etasearch is to utilize multiple
More informationResearch and Development of Data Preprocessing in Web Usage Mining
Research and Development of Data Preprocessing in Web Usage Mining Li Chaofeng School of Management, South-Central University for Nationalities,Wuhan 430074, P.R. China Abstract Web Usage Mining is the
More informationISSN: 2321-7782 (Online) Volume 2, Issue 5, May 2014 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 2321-7782 (Online) Volume 2, Issue 5, May 2014 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at:
More informationA Framework for Ontology-Based Knowledge Management System
A Framework for Ontology-Based Knowledge Management System Jiangning WU Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, China E-mail: jnwu@dlut.edu.cn Abstract Knowledge
More informationWeb Mining. Margherita Berardi LACAM. Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it
Web Mining Margherita Berardi LACAM Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it Bari, 24 Aprile 2003 Overview Introduction Knowledge discovery from text (Web Content
More informationENHANCED WEB IMAGE RE-RANKING USING SEMANTIC SIGNATURES
International Journal of Computer Engineering & Technology (IJCET) Volume 7, Issue 2, March-April 2016, pp. 24 29, Article ID: IJCET_07_02_003 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=7&itype=2
More informationChapter-1 : Introduction 1 CHAPTER - 1. Introduction
Chapter-1 : Introduction 1 CHAPTER - 1 Introduction This thesis presents design of a new Model of the Meta-Search Engine for getting optimized search results. The focus is on new dimension of internet
More informationWeb Personalization based on Usage Mining
Web Personalization based on Usage Mining Sharhida Zawani Saad School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK szsaad@essex.ac.uk
More informationSearch Taxonomy. Web Search. Search Engine Optimization. Information Retrieval
Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!
More informationA Bi-Dimensional User Profile to Discover Unpopular Web Sources
A Bi-Dimensional User Profile to Discover Unpopular Web Sources Romain Noel Airbus Defense & Space Val-de-Reuil, France romain.noel@cassidian.com Laurent Vercouter St Etienne du Rouvray, France laurent.vercouter@insarouen.fr
More informationAutomatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines
, 22-24 October, 2014, San Francisco, USA Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines Baosheng Yin, Wei Wang, Ruixue Lu, Yang Yang Abstract With the increasing
More informationCOURSE RECOMMENDER SYSTEM IN E-LEARNING
International Journal of Computer Science and Communication Vol. 3, No. 1, January-June 2012, pp. 159-164 COURSE RECOMMENDER SYSTEM IN E-LEARNING Sunita B Aher 1, Lobo L.M.R.J. 2 1 M.E. (CSE)-II, Walchand
More informationEnhancing the Ranking of a Web Page in the Ocean of Data
Database Systems Journal vol. IV, no. 3/2013 3 Enhancing the Ranking of a Web Page in the Ocean of Data Hitesh KUMAR SHARMA University of Petroleum and Energy Studies, India hkshitesh@gmail.com In today
More informationA Comparative Approach to Search Engine Ranking Strategies
26 A Comparative Approach to Search Engine Ranking Strategies Dharminder Singh 1, Ashwani Sethi 2 Guru Gobind Singh Collage of Engineering & Technology Guru Kashi University Talwandi Sabo, Bathinda, Punjab
More informationBUILDING A PREDICTIVE MODEL AN EXAMPLE OF A PRODUCT RECOMMENDATION ENGINE
BUILDING A PREDICTIVE MODEL AN EXAMPLE OF A PRODUCT RECOMMENDATION ENGINE Alex Lin Senior Architect Intelligent Mining alin@intelligentmining.com Outline Predictive modeling methodology k-nearest Neighbor
More informationOn the role of a Librarian Agent in Ontology-based Knowledge Management Systems
On the role of a Librarian Agent in Ontology-based Knowledge Management Systems Nenad Stojanovic Institute AIFB, University of Karlsruhe, 76128 Karlsruhe, Germany nst@aifb.uni-karlsruhe.de Abstract: In
More informationWeb Mining Seminar CSE 450. Spring 2008 MWF 11:10 12:00pm Maginnes 113
CSE 450 Web Mining Seminar Spring 2008 MWF 11:10 12:00pm Maginnes 113 Instructor: Dr. Brian D. Davison Dept. of Computer Science & Engineering Lehigh University davison@cse.lehigh.edu http://www.cse.lehigh.edu/~brian/course/webmining/
More informationWEB SITE OPTIMIZATION THROUGH MINING USER NAVIGATIONAL PATTERNS
WEB SITE OPTIMIZATION THROUGH MINING USER NAVIGATIONAL PATTERNS Biswajit Biswal Oracle Corporation biswajit.biswal@oracle.com ABSTRACT With the World Wide Web (www) s ubiquity increase and the rapid development
More informationAdvances in Natural and Applied Sciences
AENSI Journals Advances in Natural and Applied Sciences ISSN:1995-0772 EISSN: 1998-1090 Journal home page: www.aensiweb.com/anas Privacy Protection in Personalized Web Search 1 M. Abinaya and 2 D. Vijay
More informationAutomated Collaborative Filtering Applications for Online Recruitment Services
Automated Collaborative Filtering Applications for Online Recruitment Services Rachael Rafter, Keith Bradley, Barry Smyth Smart Media Institute, Department of Computer Science, University College Dublin,
More informationSearch Query and Matching Approach of Information Retrieval in Cloud Computing
International Journal of Advances in Electrical and Electronics Engineering 99 Available online at www.ijaeee.com & www.sestindia.org ISSN: 2319-1112 Search Query and Matching Approach of Information Retrieval
More informationHorizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis
IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 Volume 6, Issue 5 (Nov. - Dec. 2012), PP 36-41 Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis
More informationInternational Journal of Innovative Research in Computer and Communication Engineering
Achieve Ranking Accuracy Using Cloudrank Framework for Cloud Services R.Yuvarani 1, M.Sivalakshmi 2 M.E, Department of CSE, Syed Ammal Engineering College, Ramanathapuram, India ABSTRACT: Building high
More informationFlorida International University - University of Miami TRECVID 2014
Florida International University - University of Miami TRECVID 2014 Miguel Gavidia 3, Tarek Sayed 1, Yilin Yan 1, Quisha Zhu 1, Mei-Ling Shyu 1, Shu-Ching Chen 2, Hsin-Yu Ha 2, Ming Ma 1, Winnie Chen 4,
More informationClustering Technique in Data Mining for Text Documents
Clustering Technique in Data Mining for Text Documents Ms.J.Sathya Priya Assistant Professor Dept Of Information Technology. Velammal Engineering College. Chennai. Ms.S.Priyadharshini Assistant Professor
More informationQDquaderni. UP-DRES User Profiling for a Dynamic REcommendation System E. Messina, D. Toscani, F. Archetti. university of milano bicocca
A01 084/01 university of milano bicocca QDquaderni department of informatics, systems and communication UP-DRES User Profiling for a Dynamic REcommendation System E. Messina, D. Toscani, F. Archetti research
More informationBig Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network
, pp.273-284 http://dx.doi.org/10.14257/ijdta.2015.8.5.24 Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network Gengxin Sun 1, Sheng Bin 2 and
More informationSentiment analysis on tweets in a financial domain
Sentiment analysis on tweets in a financial domain Jasmina Smailović 1,2, Miha Grčar 1, Martin Žnidaršič 1 1 Dept of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia 2 Jožef Stefan International
More informationSo today we shall continue our discussion on the search engines and web crawlers. (Refer Slide Time: 01:02)
Internet Technology Prof. Indranil Sengupta Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture No #39 Search Engines and Web Crawler :: Part 2 So today we
More informationThe University of Lisbon at CLEF 2006 Ad-Hoc Task
The University of Lisbon at CLEF 2006 Ad-Hoc Task Nuno Cardoso, Mário J. Silva and Bruno Martins Faculty of Sciences, University of Lisbon {ncardoso,mjs,bmartins}@xldb.di.fc.ul.pt Abstract This paper reports
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 3, Issue 8, August 213 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Global Ranking
More informationUniversity of Glasgow Terrier Team / Project Abacá at RepLab 2014: Reputation Dimensions Task
University of Glasgow Terrier Team / Project Abacá at RepLab 2014: Reputation Dimensions Task Graham McDonald, Romain Deveaud, Richard McCreadie, Timothy Gollins, Craig Macdonald and Iadh Ounis School
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 11, November 2015 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationEffective Data Retrieval Mechanism Using AML within the Web Based Join Framework
Effective Data Retrieval Mechanism Using AML within the Web Based Join Framework Usha Nandini D 1, Anish Gracias J 2 1 ushaduraisamy@yahoo.co.in 2 anishgracias@gmail.com Abstract A vast amount of assorted
More informationWeb Advertising Personalization using Web Content Mining and Web Usage Mining Combination
8 Web Advertising Personalization using Web Content Mining and Web Usage Mining Combination Ketul B. Patel 1, Dr. A.R. Patel 2, Natvar S. Patel 3 1 Research Scholar, Hemchandracharya North Gujarat University,
More informationImportance of Domain Knowledge in Web Recommender Systems
Importance of Domain Knowledge in Web Recommender Systems Saloni Aggarwal Student UIET, Panjab University Chandigarh, India Veenu Mangat Assistant Professor UIET, Panjab University Chandigarh, India ABSTRACT
More informationQuery Recommendation employing Query Logs in Search Optimization
1917 Query Recommendation employing Query Logs in Search Optimization Neha Singh Department of Computer Science, Shri Siddhi Vinayak Group of Institutions, Bareilly Email: singh26.neha@gmail.com Dr Manish
More informationSite Files. Pattern Discovery. Preprocess ed
Volume 4, Issue 12, December 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Review on
More informationUTILIZING COMPOUND TERM PROCESSING TO ADDRESS RECORDS MANAGEMENT CHALLENGES
UTILIZING COMPOUND TERM PROCESSING TO ADDRESS RECORDS MANAGEMENT CHALLENGES CONCEPT SEARCHING This document discusses some of the inherent challenges in implementing and maintaining a sound records management
More information