Digital Research Paper Recommendation System Appling Feature Based Method

Transcription

1 Running Head: DIGITAL RESEARCH PAPER RECOMMENDATION SYSTEM APPLING 50 ICLEI Panya Siam Digital Research Paper Recommendation System Appling Feature Based Method Panya Siama, Jaiinta Yaneeb, Marung Ukrit*c Faculty of Business Administration, Maejo University, 63 M.4. Sansai, Chiang Mai, Thailand *Corresponding Author: ABSTRACT The result of internet growing is the vast information. Recommendation and Search Engine Optimization Methods are used for helping users to find their interested information. In this research, we developed the digital research paper website for assisting searchers to search the interested paper. The recommendation system was used to suggest the related papers for the searcher. The feature-based method was applied in the system. The search engine optimization method was applied to increase the ranking website on the main search engines such as Google, Yahoo, and Bing. The F-measure and Google analytic were used to evaluate the performance of the recommendation and search engine optimization process. The website was developed by using HTML and PHP languages, HTML Access, and MySQL. In the dataset, 246 papers were stored and each paper had 20 representative features. In the experimental, the results were collected between Mach and April The results showed that the recommendation and search engine optimization methods effectively assisted searchers to search for their interested papers. Keywords: Recommendation System, Data Mining, Web-Service, Search Engine Optimize Introduction Recommendation system (RS) and search engine optimization (SEO) are the tool for helping the users to search their interested information (Marung, 2013; Melville, 2002; Salter and Nick, 2006; Siriprasoetsin and Marung, 2013). The methods of the RS can be broadly categorized as content-based, collaborative filtering, featurebased, and demographic-based methods, respectively (Marung, 2013). In case of a user not need to login to the system, the recommendation system does not have enough information to create the recommendation. Hence, the feature-based method was applied to improve the performance of the system. Moreover, the search engine markets was applied to improve the website for increasing the number of searchers (Li-Hsing et

2 51 al., 2010). There are two types of the search engine markets including the pay per click (PPC) and search engine optimization. For free search markets, the SEO was applied to make the websites get better ranking on the search engine websites. In this paper, it is divided into four section. The first section describes introduction and reviews of the related research. The second section describes the methods including the recommendation, search engine optimization, evaluation methods. In the third section, the experiment results are described. In the final section, a conclusion is drawn. Methods Search Engine Optimization To reduce the digital marketing cost, the SEO is applied to improve the website. The website with high ranking get the high opportunity to be viewed by users. The part of SEO compose of (1) page rank and links optimization (2) website structure optimization. In the google search engine, the page rank value is mostly important to increase the ranking page. The page rank algorithm is calculated from incoming and outgoing links (Siriprasoetsin and Marung, 2013). The directory, webpage, keywords, and navigation were optimized the website structure (Siriprasoetsin and Marung, 2013). In the Figure 1, the title, keywords, description, and alt are the part of web structure. The HTTP Access is the tool to convert the PHP code to HTML code. The incoming and outgoing links are used to calculate the PR value. The framework of the optimization website as shown in Figure 1. To evaluate the performance of the SEO, the google analytic was applied to retrieve the number of users (Session) and Channels (Siriprasoetsin and Marung, 2013). Title Keywords Description ALT HTTP Access Tool Incoming/Outgoing Links Figure 1. The Search Engine Optimization Framework

3 52 Recommendation Methods In the proposed method, the item-based collaborative filtering and feature-based algorithms were applied (Weng et al., 2013). The system produces the top-n papers to an active searcher by calculating similarity between papers by using the cosine similarity measure as shown in Figure 2 and Figure 3. In the recommendation process, the similarities of papers are computed. Their corresponding similarities are recorded. Then, for each searcher who has clicked a set U of papers, this information is used to compute the top-n recommendation (Marung, 2013). Top-N recommendation Figure 2. Concept of the Feature-Based Top-N Recommendation System In the proposed method, the system process can be divided into three parts. Firstly, a papers-features table that stored in the database was created from the available papers as shown in Table 1. Then, the next process uses paper-to-paper similarity to compute the relations between papers by using the cosine measure as show in Equation 1. The model of the proposed recommendation system is shown in Figure 3. "#$&'#()* +,, +. = (1) Equation 1. P a is the first paper and P b is another paper. Start Papers Similarity Measure Database Recommendation Process End Figure 3. Model of the Proposed Recommendation System.

4 53 Finally, the F-measure is used to evaluate the performance of the RS (Marung, 2013; Marung, et al., 2014). The parameter in the F-measure consists of precision, recall and F1. The precision is defined as the radio of the number of paper(s) in the hit set to the number of papers in the recommendation set. The recall is defined as a radio of the number of papers in the hit set to the number of viewed paper(s). F1 is a parameter combining precision and recall. In this research, the only precision was applied to evaluate the performance because of only using and analyzing paper(s) in the basket to recommend. Calculating precision is given by 4'56#7#89 = * #*;<*=>?@ #*;<*ABC;DDBEFBF*G,GBA@* (2) Experiment Results Dataset In this session, the was developed by using HTML, PHP, and JavaScript languages. The HTML and PHP that were developed the website are the static and dynamic languages. The JavaScript is the language that process on client to decrease the time downloading process. To improve the website, the HTTP Access is tool to convert the PHP to HTML languages for the crawling robots easy to read information and to protect for the injection hacker (Hui et al., 2012). For recommendation database, there are 246 papers and 20 features stored in MySQL. The features of each paper were designed with 12 keywords, 6 branch name, and 2 others. To select the keywords, the keywords were selected by using the frequency-based method. For example, in the field of business information technology, the keywords of papers in the field are selected. Then, the frequency of keyword is ordered by descending. Figure 4 shows the top-3 keywords with high frequency that are selected into features set. In the feature-based method, a binary variable has only two states: 0 or 1, when 0 mean that the variables is absent and 1 mean that it is present as shown in Table 1.

5 54 Frequency Keywords Figure 4. Example of the Sorted Histogram Table 1 Example of Binary Values f 1 f 2 f 3 f n p p p p m f n and p m are the number of features and papers, respectively. SEO Results The google analytics ( is tool to evaluate the performance of the SEO process. The result was collected between 1/3/2558 and 30/4/2558. In the result of incoming links, there are 961 sessions from 43.1 direct, 37.7 organic search, 16.8 Social network, and 2.4 referral as shown in Figure 6. Figure 5. Session of Users.

6 55 Figure 6. Top Channels. Recommendation Results The system performance was evaluated by using the F-measure (Marung, 2013; Marung, 2014). In the experiments, precision parameter was selected to evaluate the system performance because the system uses the only information in the basket (not Sing in). The precision results demonstrate average precision with the one, two, and three papers in the basket are 20, 40, and 60, respectively. The limitation of the study is most of users view one of the paper from the recommendation. Figure 7. Top-5 Recommendation Conclusion To help the researcher, the recommendation system and website optimization were developed. There are two parts to optimize the website. The first part is the recommendation system by using the feature-based method. The cosine similarity measure is used to calculate the similarity between papers in the basket and papers in the database. The results of calculating are the input of the recommendation process. The second part is the website optimization by using the SEO method. To develop the website, the HTML and PHP languages, HTTP access, MySQL, and JavaScript were applied. There are 246 and 20 features stored in the database. To evaluate the system

7 56 performance, the F-measure and google analytic were applied. The result of SEO was shown that there are 43.1, 37.7, 16.8, and 2.4 come from direct, organic search, social network, and referral, respectively. The results of recommendation system show that the feature-based methods effectively assisted searchers.

8 57 References Cui, M., & Songyun, H Search Engine Optimization Research for Website Promotion, Information Technology, Computer Engineering and Management Sciences (ICM), Hui, Z., Shigang, Q., Jinhua, L., & Jianli, C Study on Website Search Engine Optimization, International Conference on Computer Science & Service System (CSSS), Li-Hsing, H., Jui-Chen, H., Meng-Huang, L., & Hui-Yi, H The Application of Search Engine Optimization for Internet Marketing: An Example of the Motel Websites, The 2nd International Conference oncomputer and Automation Engineering (ICCAE), Marung, U A Novel Clustering Method and Its Application to E-Commerce Recommendation Systems, Unpublished doctoral dissertation, Chiangmai University, Thailand. Marung, U., Theera-umpon, N., and Auephanwiriyakul, S Applying memetic algorithm-based clustering to recommender system with high sparsity problem, Journal of Central South University, 21(1), Melville, P. N Content-Bossted Collaborative Filtering for Improved Recommendations, Eighteenth national conference on Artificial intelligence, Salter, J., & Nick, A CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering, IEEE Intelligent Systems, 21(1), Siriprasoetsin, P., & Marung, U Search Engine Optimization of Daculty of Business Administration Website, International Conference on Social Science and Management, Hui, Z., Shigang, Q., Jinhua, L., & Jianli, C Study on Website Search Engine Optimization, International Conference on Computer Science & Service System (CSSS), Weng, S.-S., & Mei-Ju, L Feature-Based Recommendations for One-to-One Marketing, Expert Systems with Applications: An International Journa, 26(4)