Our study suggests a new value-added service for mobile phone applications. Dispatches of customized blog articles not only enable mobile phone users to enjoy reading blog articles without time and venue limitations but also provide a proactive channel to deliver the blog contents instead of passive browses from the users.
Owing to their dramatic growth in recent years, blogs have become a dominant medium on the Internet. Although RSS feed is a common solution to receive the latest contents automatically, there are still some problems, including lack of subscriptions and overloaded information. For that reason, filtering the articles so they are suitable for each user is an important issue. In the past, studies about blogs centered on bloggers (the authors) but ignored the views of mass readers. Our study applies the co-RSS method based on the notion under Web 2.0. Our proposed CCS can predict the trend of time-sensitive popularity of blogs. The m-CCS is grounded on the topic clusters of the blog articles which represent the perspectives of the authors. The m-CCS also considers the aspects of the readers’ click rates to trace the popularity trends of the topic clusters from the affluent blog contents. Moreover, as regards mobile phone users, the m-CCS will analyze their browsing behaviors and personal preferences to recommend their preferred popular blog topics and articles.
Our experiment evaluations show that the proposed methods can effectively increase the hit ratio of customers who use their mobile phones to read blog articles. Considering customized predictive popularity degree is effective in improving the quality of recommending blog articles to mobile users. Moreover, the harmonic-mean approach is more effective than the weighted approach in deriving the customized predictive popularity degree of topic cluster for target user.
Recommended article list is arranged according to the predicted user’s preference scores on articles. The order of the recommended article list will affect the mobile users’ reading
behaviors on the mobile phone. Generally, the top ranking article may have higher click rate, since the mobile phone with small screen is difficult to scroll. Mobile users may have different degrees of preferences in browsing articles; users usually show more interest in those articles being clicked earlier. Our future work will consider user feedback on browsing recommended articles to adjust the prediction scores. For example, if a mobile user clicks the lower ranking articles first, it denotes that the user may have more interest in those articles, and thus we should put more weight in those articles during the process of inferring user preferences and making predictions.
Moreover, our recommendation approach considers item-based CF, attention degrees of articles and customized predictive popularity degrees of topic clusters, with an attempt to balance the trade-off in recommending existing articles and new articles. Some mobile users may always pursue the newest and hottest articles, while some mobile users may be interested in those articles which are worth reading even though they are not new articles. Further study is required to investigate effective recommendation approach that considers mobile users’
preferences toward browsing existing and new articles.
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