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Boosting Memory-Based Collaborative Filtering Using Content-Metadata

Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea
Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea
Author to whom correspondence should be addressed.
Symmetry 2019, 11(4), 561;
Received: 11 March 2019 / Revised: 11 April 2019 / Accepted: 14 April 2019 / Published: 18 April 2019
PDF [5760 KB, uploaded 18 April 2019]


Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment. View Full-Text
Keywords: collaborative filtering; content-metadata; user-content rating collaborative filtering; content-metadata; user-content rating

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kim, K.S.; Chang, D.S.; Choi, Y.S. Boosting Memory-Based Collaborative Filtering Using Content-Metadata. Symmetry 2019, 11, 561.

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