Cold-Start Recommendation with Provable
Guarantees: A Decoupled Approach
Abstract— Cold-Start Recommendation with Provable Guarantees: A Decoupled Approach. The matrix completion paradigm provides an appealing solution to the collaborative ﬁltering problem in recommendation systems, some major issues, such as data sparsity and cold-start problems, still remain open. In particular, when the rating data for a subset of users or items is entirely missing, commonly known as the cold-start problem, the standard matrix completion methods are inapplicable due the non-uniform sampling of available ratings. In recent years there has been considerable interest in dealing with cold-start users or items that are principally based on the idea of exploiting other sources of information to compensate for this lack of rating data. In this paper, we propose a novel and general algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, our proposed recommender algorithm, dubbed < Final Year Projects 2016 > DecRec, decouples the following two aspects of the cold-start problem to effectively exploit the side information: (i) the completion of a rating sub-matrix, which is generated by excluding cold-start users/items from the original rating matrix; and (ii) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference prevents the error propagation of completion and transduction, and also signiﬁcantly boosts the performance when appropriate side information is incorporated. The recovery error of the proposed algorithm is analyzed theoretically and, to the best of our knowledge, this is the ﬁrst algorithm that addresses the cold-start problem with provable guarantees on performance. Additionally, we also are able to apply our algorithm in situations where both cold-start users and items are present simultaneously. We conduct thorough experiments on real datasets that complement our theoretical results. These experiments demonstrate the effectiveness of the proposed algorithm in handling the cold-start users/items problem and mitigating data sparsity issues.
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