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, Croatia {dario.vuljanic, lidia.rovan, mirta.baranovic}@fer.hr Abstract. This paper gives an overview of web personalization approaches and techniques and explains possible ways of introducing the semantics into the web personalization process. Theoretical models of personalization are presented as base models for different semantic approaches to personalization and evaluated from aspect of usability. Existing semantic personalization models and systems are described and the level of improvement they achieve is discussed, with emphasis on personalization accuracy. The initial idea for including personalization into the existing semantic web portal "Sweb" is presented. Proposed approach combines social networking data with semantic reasoning based personalization technique to increase the accuracy of recommendations. Keywords. Web personalization, semantic personalization, semantic web, content filtering, recommender systems 1. Introduction To personalize means to make or change something so it is suitable for a particular person [9]. Personalization is defined as the ability to provide content and services tailored to individuals based on knowledge about their preferences and behavior [7]. Web personalization is about personalizing aspects of web resources - the content itself, links, web page structure and navigation. Nowadays, when information overload is one of the common problems of web usage, users find it difficult to distinguish relevant from irrelevant information. The main goal of personalization is to help users find the information they are interested in, what can significantly enhance their web experience. Most of personalization systems try to filter available content by user's preferences and recommend only content found potentially interesting for that particular user. The most difficult aspect of personalization is to understand user's preferences and to use them in an intelligent way for content filtering. The concept of semantic web exists for more than a decade as an idea, but it is not used widely in practice yet. Examples of successful semantic projects exist, but the semantic web is still waiting for its killer app, to increase the semantic web popularity. In personalization, technologies of semantic web are used mostly in research projects. Various approaches to semantic personalization are being proposed, evaluated and discussed, each of them with its own strengths and weaknesses. In this paper we propose an approach for including personalization into the semantic web portal "Sweb". Existing personalization approaches were examined in order to find a personalization method most suitable for personalizing the "Sweb" portal. As the result of our research, new personalization system is designed, based on semantic reasoning combined with social networking. This paper is organized as follows. Section 2 briefly describes semantic web portal "Sweb". An overview of web personalization approaches is given in section 3. Semantic personalization approaches are described in section 4. Our idea for personalization of the "Sweb" portal is presented in section 5. Section 6 contains conclusion and guidelines for further work. 2. Semantic web portal "Sweb" Semantic web portal "Sweb" [6] is a news portal aimed for students, based on RSS (RDF Site Summary) feed services. The portal integrates data from the academic life and data about social and cultural manifestations, such as concerts or theatre plays. Also, the portal contains a calendar with user's events and the support for popular web 2.0 services, such as Facebook and Google Calendar. When it comes to heterogeneous data, from multiple data sources, users can find it hard to find the information they need. Personalization of the "Sweb" portal tends to improve user 217 Proceedings of the ITI 2010 32 nd Int. Conf. on Information Technology Interfaces, June 21-24, 2010, Cavtat, Croatia
experience by filtering available data in accordance to user's preferences. A recommender system that suggests content (news and events) suitable for a particular user, is supposed to be integrated into the existing solution. 3. Web personalization approaches and techniques 3.1. Current trends in web personalization Web personalization is currently very popular aspect of improving user's browsing experience. Popular web services, such as Amazon.com and Facebook have included personalization in their everyday use. Users get recommendations regarding an item they bought or news their friends liked. Latest improvements of Google search engine also include additional personalization feature, called Stars [11], enabling the user to mark preferred items. While traditional personalization methods are already widely used, semantic personalization takes place mostly in research projects [4,7,8,12]. 3.2. Approaches to web personalization In the personalization process, various approaches are combined to suit user's preferences as accurate as possible. Anand and Mobasher [1] classify approaches to web personalization by data they utilize, the way of processing data, interaction with the user, the learning paradigm and the place where personalization process is done. Most commonly, personalization approaches are classified by data they utilize. Individual approach predicts user's preferences using data from past interactions solely with that user. On the other hand, collaborative approach relies on data about user's neighborhood, consisting of users with similar preferences. From user's aspect, proactive approach is welcomed, because it tends to automatically collect data, without disturbing the user. Reactive approach can be intrusive because it demands data about preferences explicitly provided by user. Each personalization approach has its advantages and disadvantages. Generally best approach does not exist. Depending on the system being personalized and user's needs, approaches should be balanced to produce the most suitable solution for the given situation. 3.3. Web personalization techniques According to [1] and [4], web personalization techniques are classified in five classes: content based filtering, traditional collaborative filtering, model based techniques, hybrid techniques and semantic techniques. Content based filtering uses an individual approach which relies on user's ratings and item descriptions. Items having similar properties as items positively rated by user are being recommended to the user. The most common problem of content based filtering is the new user problem. This problem occurs when a new user is added to the system, hence has an empty profile (without ratings) and cannot receive recommendations. Traditional collaborative filtering uses ratings from user's neighborhood. Neighbors are users who provided similar ratings for same items. Item is being recommended to the user according to the overall rating of the neighborhood for that item. Problems in collaborative filtering occur when new content item is added to the system, because the item cannot take place in personalization without being rated before. Model based techniques represent an improvement in scalability issues, because part of data is pre-processed and stored as model, which is used in the personalization process. Hybrid personalization techniques combine two or more personalization techniques to improve the personalization process. In most cases, content based filtering is combined with traditional collaborative filtering. Collaboration via content is an example of a hybrid personalization technique, where user profiles contain item descriptions based on similarity of user's. Traditional personalization techniques can provide very suitable solution for tailoring web pages according to user's preferences. On the other hand, traditional web personalization has limitations in accuracy of modeling user's behavior. The potential of semantic personalization is in better understanding of user's preferences. Semantic attributes and annotations provide extra information, which can improve the intelligence of the personalization process. Key goal of using semantic enhancements in personalization is modeling user's behavior more precisely. 218
4. Semantic personalization Semantic personalization represents an expansion of traditional personalization techniques with semantic web technologies. The simplest example of semantic personalization is usage of semantic annotations for content items and semantic similarity, instead of lexical similarity used in traditional personalization techniques. More complex examples of semantic personalization, described in this chapter, use various semantic capabilities to improve the process and the result of personalization. 4.1. Technologies of semantic web One of the key concepts of semantic web, also used in semantic personalization, is ontology. Gruber proposed very simple definition, defining ontology as specification of a conceptualization [5]. Typically, ontology consists of a finite list of terms describing domain of interests and relationships between these terms. The most common relationships in ontologies are generalization and specialization, as shown in Fig. 1. Apart from relationships, ontologies may also include information such as properties or value restrictions. Ontologies are described with semantic technologies: Resource Description Framework (RDF), RDF Schema (RDFS) and Web Ontology Language (OWL) [3]. Figure 1. An ontology hierarchy example Although commonly considered to be a language, RDF is essentially a data model based on the principle of subject-predicate-object triplets, called statements. RDF was made for metadata descriptions, containing attributes about elements and describing relationships between those elements. RDF is domain independent, which means it can be used for modeling of any domain. To define the vocabulary for a certain domain, containing element descriptions for that domain and usage rules, RDFS is used. OWL is a language considered to be an improvement of the RDF, with the same purpose but with richer capabilities. It is based on elements such as classes, hierarchies, attributes, restrictions etc. Three different specification of the language exist, with different capabilities and compatibility with RDF: OWL Full, OWL Description Logic and OWL Lite. The Friend of a Friend (FOAF) [14] ontology is one of the most popular ontologies used across the web. FOAF describes people, their activities and relationships with other people and objects. Using FOAF each person is uniquely identified with a URI and described with RDF and OWL. Increasing number of FOAF profiles encourages developers to create tools for managing FOAF profiles. In the context of web personalization, FOAF profiles represent user friendly personalization enhancement, with capabilities for collaborative filtering based on friendship network. 4.2. Semantic personalization techniques Different techniques and approaches to semantic personalization exist as the result of research projects. This chapter describes some of semantic approaches to personalization, which were considered to be included in our approach to personalizing the "Sweb" portal. Semantic expansion approach to content based filtering, introduced by Liang et al. [7], tends to improve keyword based document recommendation with semantics. Semantic tree, representing correlations between domain concepts, is used as the base structure. To anticipate user's interest into each concept, user's ratings for items are used. Liang's approach uses semantic network activation spreading model, starting from concepts of user's interests and spreading to concepts of potential user's interests. Spreading inside the semantic network is based on relationships between concepts and principles of generalization and specialization. According to the study, this semantic expansion approach models user's preferences more accurate than the traditional content based approach. Nevertheless, some disadvantages of the content based filtering 219
remain unsolved, such as the new user problem and the problem of disturbing the user with document ratings. FOAF based approach to semantic personalization was introduced by Ankolekar and Vrandecic [2]. Their approach tends to exploit the popularity of FOAF profiles in web personalization process. FOAF based personalization model is built upon an existing content based filtering or similar model and represents an enhancement which should improve traditional approach with information from the user's FOAF profile. An extension of standard HTTP protocol is proposed, to enable distribution of FOAF profiles. The FOAF based personalization is promising because it uses a technology already accepted from users and does not make pressure on user to rate content items. A major disadvantage of this approach is the privacy problem, because FOAF profile can contain private data. personalization technique is reasoning, where an existing data are processed with the goal of finding unobvious connections between the user and the contents. The concept of the ontology profile, shown in Fig. 2 [4], was introduced as the best solution for user modeling in this hybrid system. User profiles contain references to content items in the domain ontology and numerical measures of user-item correspondence called DOI index (DOI = Degree Of Interest). DOI index can take values in range [-1,1], where -1 represents absolute negative correspondence and 1 absolute positive correspondence. Reduced storage size demands and adaptability to any domain are marked as major advantages of the ontology profile. The new user problem remains yet to be solved in this approach. Ma et al. [8] propose an approach that combines user's social network information and rating records to improve prediction of user's behavior. Authors find including social relations between users into the personalization process very important, because they believe that user's social connections affect user's actions. When comparing recommendations from friends and recommender systems, users prefer recommendations from their friends. Major advantages of their approach are scalability and improved accuracy of the personalization process. 5. Personalizing semantic web portal "Sweb" Figure 2. User modeling example based on the principle of the ontology profile Y. Blanco-Fernández et al. [4] designed a hybrid reasoning based approach to semantic personalization and tested the implementation of the recommender system in a TV web portal. Their recommender system combines content based filtering and collaborative filtering with the semantic technologies for ontological domain modeling. The central process of this hybrid After examining existing personalization approaches, we decided to build the "Sweb" personalization system upon the semantic reasoning based approach described in [4]. Low space costs for storage of ontology profiles and proven efficiency of the personalization were key factors that led us to that decision. Although this approach was proven to be quite efficient, we believe that precision of recommendations can be improved by integrating the process with social networking data. Social networks are the most popular aspect of web 2.0 and in accordance to Ma et al. approach, we assume that social connections significantly determine and describe user's interests. Because the "Sweb" portal is built using semantic web technologies, we decided to use FOAF profiles for storing data about user's social network. The "Sweb" portal personalization model is presented in Fig. 3. As stated before in this paper, the "Sweb" portal integrates content from 220
multiple RSS feed services, which represent primary input into the portal. In personalization, additional data is used: user's FOAF profile and stored ontology profiles for that particular user and his friends. While the ontology profile contains individual data about user's interest into the content items, user's FOAF profile represents a social part of the complete profile. User's FOAF profile contains list of user's friends, representing user's social network. The FOAF profile is dynamically generated from user's account on Facebook or Twitter, depending on user's preferences. In the personalization process, ontology profiles are used to predict degree of interest to available content for the user, as described in [4]. Additionally, collaborative personalization phase is executed, having list of user's friends from the FOAF profile as user's neighborhood. Finally, recommendations from the individual phase and the collaborative phase are combined together and top rated items are recommended to the user. system for recommended content. User will have three options: not to rate the item, rate the item positively or rate the item negatively. We believe this approach provides enough information about user's satisfaction with recommendations and there is no need to use a rating system with wider scale (e.g. ratings 1-5). When it comes to usability, according to [15] binary (like/dislike) rating systems are suitable for personalized recommendations. Also, binary rating systems provide polarized results [15], clearly distinguishing user's satisfaction. Along with the binary rating system, we propose usage of an automatic data collector. The purpose of an automatic data collecting subsystem is to implicitly retrieve user's feedback (e.g. when user follows the RSS link to the original news it means that user is interested in that news item). The new user problem is mitigated in our approach, because it can occur only in situation when neither the user nor any of the user's friends have accessed a single content item. If an ontology profile exists for the user or any of his friends, the user can receive personalized recommendations. Although the process of personalization can start almost instantly after receiving any feedback from the user, the results of personalization at that moment will probably not be satisfactory due to the lack of data. On the other hand, we expect the level of quality for recommendations to increase quickly along with growth of the ontology profile data, especially for users with large social network. 6. Conclusion and further work Figure 3. Architecture of the personalized "Sweb" portal For dynamic generation of FOAF profiles, we propose existing available services: Facebook FOAF Generator [10] and Semantictweet [13]. Both services have very similar functionality. Facebook FOAF Generator accesses user's Facebook profile and creates a FOAF file which contains general information about the user and user's friends. Semantictweet creates very similar FOAF profile, which contains information about user's connection on Twitter. In order to collect explicit feedback from the user, we propose usage of Facebook-like rating Although some aspects of personalization are widely used across the web, complete and complex personalization systems are rarely seen in practical use. More often, intelligent techniques for personalization are used in research projects, resulting with some answers about personalization and with even more new questions waiting to be answered. That is the case especially when it comes to semantic personalization techniques. On the other hand, if the intensive research of the semantic personalization continues, it should be only matter of time for the semantic personalization to be accepted and used across the web. While some problems can be resolved within research projects, some problems will remain unsolved until semantic personalization becomes a part of everyday use. Only users can give answers about accuracy and usability of the personalization 221
process. Semantic approach to personalization is considered to have great potential for improvements, but end users will have the final word on that. In this paper we described the results of our research in web personalization and proposed the model of a personalization system for the semantic web portal "Sweb". By combining social networking data with the semantic reasoning based approach to personalization we hope to increase the accuracy of recommending items of user's interest to the user. As further work, we plan to make a prototype of the proposed personalization model and integrate it to the "Sweb" portal. After the integration is done, our recommender system will be tested with real users. We hope to gather feedback from the users about their personalized experience, to be able to evaluate the recommender system and determine possible improvements in our approach. 7. References [1] Anand SS, Mobasher B. Intelligent Techniques for Web Personalization, first chapter of the book Intelligent Techniques for Web Personalization. Springer Berlin / Heidelberg; 2005. [2] Ankolekar A, Vrande i D. Personalizing Web Surfing with Semantically Enriched Personal Profiles. http://www.aifb.unikarlsruhe.de/wbs/dvr/publications/swp200 6.pdf [01/03/2010]. [3] Antoniou G, van Harmelen F. A Semantic Web Primer - second edition. The MIT Press; 2008. [4] Blanco-Fernández Y, Pazos-Arias JJ, Gil- Solla A, Ramos-Cabrer M, López-Nores M, García-Duque J, Fernández-Vilas A, Díaz- Redondo RP. Exploiting Synergies Between Semantic Reasoning and Personalization Strategies in Intelligent Recommender Systems: A Case Study. The Journal of Systems and Software 2008; 81(12): 2371-2385. [5] Gruber T. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies 1995; 43(5-6): 907-928 [6] Jagušt T. Students Portal Based on Semantic Web, thesis. Faculty of Electrical Engineering and Computing, University of Zagreb; 2008. [7] Liang T-P, Yang Y-F, Chen D-N, Ku Y-C. A Semantic Expansion Approach to Personalized Knowledge Recommendation. Decision Support Systems 2008; 45(3): 401-412. [8] Ma H, Yang H, Lyu MR, King I. SoRec: Social Recommendation Using Probabilistic Matrix Factorization. Proceeding of the 17th ACM conference on Information and knowledge management; 2008 October 26-30; Napa Valley, California, USA. ACM, New York, NY, USA; 2008. p. 931-940. [9] Macmillan Dictionary, http://www.macmillandictionary.com/dictio nary/british/personalize [01/31/2010]. [10] Matthew Rowe - PhD Student, http://www.dcs.shef.ac.uk/~mrowe/foafgen erator.html [03/28/2010]. [11] Official Google Blog: Stars make search more personal, http://googleblog.blogspot.com/2010/03/sta rs-make-search-more-personal.html [03/30/2010]. [12] Poppe C, Martens G, Mannens E, Van de Walle R. Personal Content Management System: A Semantic Approach. Journal of Visual Communication and Image Representation 2009; 20(2): 131-144 [13] SemanticTweet - twitter meets the semantic web, http://semantictweet.com/ [03/30/2010]. [14] The Friend of a Friend (FOAF) Project. http://www.foaf-project.org/ [01/27/2010] [15] Thumbs Up/Down Style Ratings - Social Patterns, http://www.designingsocialinterfaces.com/p atterns.wiki/index.php?title=thumbs_up/d own_style_ratings [03/31/2010] 222