Abstract

Information overload on the Web is a well recognized problem [1], where users find it increasingly difficult to locate the right information at the right time. Recommender system [2, 3] comes to the rescue for such a consumer. However, despite all advances, the current generation of recommender systems still requires further improvements to make recommendation methods more effective and applicable to an even broader range of real-life applications. We propose and investigate CER system, a Community Expert based Recommendation system. The paradigm is realized by an Interest Mining module which defines a constructing algorithm for Interest Group by uncovering shared interest relationships between people, using their blog document entries and interest similarity relations. Once the interest similarity group is constructed, then we identify an expert from each of the groups so formed. Expert identification from the Collaborative Interest Group is the key to recommendation as it is only the expert’s blog whose recommendation is considered compared to systems which require a large set of customer preferences for predicting the new preferences accurately for effective Collaborative filtering-based recommendation, solving the most prominent problem existent in collaborative filtering, the First-Rater or the cold- start problem. The initial results show that the CER is a motivating technique.

References

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