Web2.0 is a web technology that facilitates information delivery through a collaborative and social-related manner. Web.2.0 created several new business models [24], such as social bookmarking, social tagging, and blogs. In this research, we implement personalized blog article recommendations in a social bookmarking website.
2.1.1 Blogs
Nowadays, blogs have already become a social media for people to express themselves. People use blogs to share their findings, communicate with friends, and express their opinions [11].
Some studies focused on link structure analysis on blogs, for example, Kritikopoulos et al. proposed a method to find the social relationship between bloggers by analyzing link structure [15]. Agarwal et al. identified the influential bloggers in a community by analyzing blog cross-links [3].
Several researches focused on analyzing blog content, to discover valuable information, including categorizing blogger's interests based on short snippets of blog posts [19], and identifying bloggers‟ emotion ratings from short blog posts [8]. Blog post had been analyzed to recommend suitable tags [32], and automatically predict trends [9].
A variety of methods has been proposed for user modeling and personalized recommendation in blog space. For example, Liu et al. [20] classified articles into predefined categories to identify authors‟ preferences, and thus to automatically recommend blog articles which are suitable for their interests by analyzing the contents
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which bloggers have acted on. Tsai and Liu [35] recommended blog articles for mobile applications by analyzing the popularity trend of blog topics. Tsai et al. [36] combined semantic tagging and personal social model to recommend blogs. Huang et al. [10]
proposed an approach to extract relevant terms from blog articles associated with users, and then recommend blog articles explored by Google‟s search engine.
Nevertheless, most research did not consider user reputations and popularity degree of blog articles. We analyze user‟s push (recommend) behavior in social bookmarking website to derive user reputations. The reputation-based popularity degrees of blog articles are derived based on user reputation, and are userd as the kernel of our recommendation approach.
2.1.2 Social Bookmarking
Social bookmarking via web-based systems enables users to manage their bookmarks of web pages. Famous social bookmarking sites like Slashdot.org and digg.com have their own model of reputation where users with extensive authorship and recommendation are promoted to being moderators and super-moderators [33]. Current research include Klaisubun et al. [14] , who proposed an analysis of users‟ behaviors in discovering useful information resources through social bookmarking services.
Puspitasari et al.[27] applied social bookmarking in digital library system, combining comments and ratings to help people find objects of quality.
funP (http://funp.com) [18] is a company that provides bookmarking service in Taiwan, allowing people to share their articles and recommend other people‟s articles through the web platform. In funP, hot articles are separated and organized into different
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categories. When users publish their articles, they have to choose one main category, such as “travel”, and then they will be asked to provide some tags describing the topics of their articles. funP only provides „hot‟ article recommendations, such that every user receives the same article recommendations regardless of his/her interest. Traditional social bookmarking sites only promote the hottest article, without considering user interests and reputation.
2.2 Recommender Systems
As e-commerce prospers, an overwhelming amout of information flows through the Internet has cause the problem of information overload. Given this problem, recommender systems have emerged in various applications to provide assistance [22, 28].
2.2.1 Content-based Filtering
The Content-based filtering (CBF) approach analyzes users‟ preferences on the attribute features of item to build up a personal feature profile, and then predict which items the user will like [6]. Content-based filtering (CBF) has been used mainly in the context of recommending items such as web pages and news articles, etc, by analyzing their content descriptions. The content is parsed, and item features are extracted to establish a characteristic profile. Items that were previously liked by a user are used to generate a user profile. Therefore, to pre-process the item content, the content-based recommender systems depend heavily upon the techniques of information retrieval. The limitation of the CBF approach is that users can only receive the recommended items which are similar to the past.
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2.2.2 Collaborative Filtering
Based on the relationship between items or users, CF method can be classified into two types [30], user-based CF (UCF) and item-based CF (ICF). The UCF exploits historical data expressing preferences to form user neighbors and make recommendations based on those similar users‟ opinions. ICF [31] analyzes the similarities between items, which are based on user‟s ratings among items. Then, the item similarities are used to compute recommendations for a user by finding items that are similar to those items the user has liked previously. A famous example is Amazon.com that recommends similar items to the customer based on past records [17].
2.2.3 Reputation System
A reputation system collects, distributes, and aggregates feedback about participants’ past behavior [28], allowing users to maintain trust in a dynamic environment. Many researchers proposed to use reputation as an auxiliary factor in the recommending phase. Opinion leader in a group can be identified by using the reputation system [26]. Adler and Alfaro [1] presented a content-driven reputation system to derive the Wikipedia author's reputation.
In addition, link analysis algorithm such as PageRank algorithm has been applied to derive users‟ reputation in a user interactive question-answering system [7]. Google‟s PageRank algorithm derives the importance web pages by computing the PageRank score of a webpage, which is basically derived from the PageRank scores of those web pages pointing to the web page [4].
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