A Simple But Effective Method to Incorporate Trusted Neighbors in Recommender Systems
Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this paper, we propose a simple but effective method, namely “Merge”, to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user’s trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations.
KeywordsActive User Recommender System Data Sparsity Similarity Threshold Collaborative Filter
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