Alleviating the Sparsity Problem in Recommender Systems by Exploring Underlying User Communities

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Provided by: RWTH Aachen University
Topic: Data Management
Format: PDF
Collaborative filtering, one of the main recommender systems' approach, has been successfully employed to identify users and items that can be characterized as similar in large datasets. However, its application is limited due to the sparsity problem, which refers to a situation where information to infer similar users and predict items is missing. In this paper, the authors address this by detecting underlying user communities that aggregate similar tastes and predicting new relations within communities.
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