RSS Feed Recommendation

نویسنده

  • Charles Chen
چکیده

Introduction Really Simple Syndication (RSS) Feeds allows users to access blogs and articles in an easy to read format. It cuts out the overhead of navigating websites for content and allows users to get information more quickly. Currently, the user is in total control of their RSS feeds, adding and deleting feeds according to their tastes. This requires the user to actively search out RSS feeds that interest them. The proposed recommendation algorithm will aid users in their search for new and exciting feeds. It will suggest feeds by extracting features from the user's subscription history and predict the feeds they will most likely enjoy. Because there are many recommendation algorithms, it is important to choose an algorithm that is suitable for this problem and the data set. Therefore, I begin by describing the data set in Section I. Then I discuss the results of using a simple recommendation algorithm that utilizes K-Means Clustering in Section II. The disadvantages of clustering will motivate the use of Maximum Margin Matrix Factorization (MMMF) [1] for this problem in Section III.

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تاریخ انتشار 2010