1. INTRODUCTION
Web2.0 represents a new generation of web-based communities for internet innovation. Through Web2.0, information is delivered more collaboratively in a social-related way [34]. Web2.0 services, including social bookmarking, social tagging, blog, and Wikipedia, are valuable collections of human knowledge that are created by users in a collaborative manner. For example, people can share their daily lives in Blogger, chat little things in Facebook, search collaborative editing knowledge in Wikipedia, and tag funny pictures in Flicker, etc. In other words, Web 2.0 social relationships bring us a new way of sharing.
Blog is a web page that serves as a publicly accessible note for an individual or a group of people. With the rapid growth of bloggers and blog articles, the vast amount of information brings the phenomenon of information overload [16]. As a result, it is an ideal place to provide recommendation service in the blog platform, especially for the purpose of finding valuable blog articles.
Social bookmarking provides the service of article recommendation for popular blogs based on the number of people that like the blog articles. funP (http://funp.com/) is a popular social bookmarking Web site in Taiwan. This site enables users to discover and share contents from blogs on the web. Users can share their own blog articles or recommend other people‟s articles. The ease of sharing for mass users in social bookmarking site, results an overwhelming amount of articles, making the selection of desirable articles increasingly difficult for users.
A recommender system is a solution to the problem of information overload [2].
Recommender systems are widely used to provide suitable personalized information to
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users according to their preferences [13]. Generally, recommender systems mainly include Content-based filtering (CBF) and Collaborative filtering (CF) approaches. The CBF approach analyzes the users‟ preferences on the attribute features of item to build up a personal feature profile, and then predict which items the user prefer. The CF exploits historical data expressing preferences to form user neighbors or item neighbors, and makes recommendations based on those similar users‟ opinions or similar items [6].
Moreover, reputation systems have been integrated with recommender systems to enhance recommendation quality [22]. Reputation systems generally analyze user interactions to derive the reputation scores of users from his/her past behaviors [28].
There are two categories of reputation systems, one computes the reputation scores based on users‟ past ratings on items [23], whereas in social bookmarking site, there are no ratings. The other category of reputation systems considers human relationships to derive user preferences by presuming a user‟s preference similar to his/her friends‟ preferences [21]. However, human relationships need to be explicitly specified and are difficult to obtain.
In a social bookmarking web site, the users usually have two roles. The first role, namely the publishers can publish and push (recommend) their own articles or other users‟
articles to the web site. The second role, namely the followers who also like the published articles, can push the published articles to express their recommendation. In this paper, we use the push-follower relationships to form a reputation network and derive the reputation scores of users. Generally, a user with more followers will have higher reputation scores. The web site provides the push counts of articles indicating the recommended popularity degrees of articles. Thus, users can refer the push counts to find
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popular and interesting articles. Popularity based solely on push counts, however, cannot truly reflect the trend of popularity. The articles pushed by highly reputed users are more likely to become popular than those articles pushed by users with lower reputation scores.
Thus, we propose to derive the popularity degree of an article by considering the reputation scores of users that push the article. In addition, users may have different interests in the emerging popular articles. Accordingly, we propose a personalized blog article recommendation approach, which combines the reputation-based popularity with content based filtering, to recommend desirable articles to users that satisfy popularity and personal interests.
A variety of methods has been proposed to model the blogger‟s interest, such as classifying articles into predefined categories to identify the author‟s preference [19].
Bloggers can receive the recommended content which is similar to their earlier experiences.
Although existing researches have proposed content-based filtering or collaborative filtering approaches to recommend desirable blog articles that satisfy user preferences, they did not address the issue of recommending personalized popular articles in social bookmarking web sites. Existing recommendation approaches did not consider the recommended popularity degrees of articles, and did not investigate the issue of deriving recommended popularity degrees of articles by considering user reputations.
Accordingly, our approach combines reputation-based popularity with the content-based filtering to enhance the quality of recommending personalized and popular blog articles.
Our experimental results show that the proposed approach outperforms conventional approaches.
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The remainder of this paper is organized as follows: Session 2 introduces the related works about web2.0 and recommender systems. Our proposed method is given in Section 3. Section 4 shows our experiment results. The conclusions are finally described in Section 5.
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