A Movie Recommendation Model: Solving Cold Start Problem

doi:10.3969/j.issn.1006-2475.2018.11.016

Abstract

Abstract: The rating data of the recommended system database is scarce, and the quality of the movie recommendation is limited. To solve this problem, a model that simultaneously incorporates user and movie metadata into an improved implicit semantic model is proposed. The user metadata-classification matrix and movie metadata-classification matrix are constructed, and the classification domain and the implicit factor space are mapped to obtain the hidden factors of the new user and the new movie, and a recommendation is made. The experimental results show that this model can effectively solve the cold start problem while improving the accuracy of prediction.

Key words: recommender system, cold start, metadata, latent semantic model, movie recommendation

CLC Number:

TP391

LIU Chun-xia， LU Jian-bo， WU Ling-mei. A Movie Recommendation Model: Solving Cold Start Problem[J]. Computer and Modernization, 2018, 0(11): 83-.

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