Research on a commodity recommendation algorithm based on reverse furthest neighbor

doi:10.3969/j.issn.1000-5641.2019.03.008

Abstract

Abstract: Effective recommendation algorithms can help maximize the value of a product. By studying the user's preferences, we can recommend underlying contents for the user from mass merchandise information. However, most recommendation systems focus on popular products, ignoring those products that are currently not popular but have huge potential. Our recommendation system, based on reverse furthest neighbor (RFN) queries, uses the idea of mining popular products in niche markets. We improve the traditional collaborative filtering recommendation algorithm and adopt a collaborative filtering algorithm based on expert users. The modified algorithm can recommend products with potential value based on the power law, which makes the distribution of minority mined products more visible to users. Experimental results show that the quality of the proposed algorithm is high and is suitable for partially addressing the long tail problem.

Key words: recommendation system, collaborative filtering, reverse furthest neighbor (RFN), power law

CLC Number:

TP391

WANG Kai, LI Bo-han, WAN Shuo, ZHANG An-man, GUAN Dong-hai. Research on a commodity recommendation algorithm based on reverse furthest neighbor[J]. Journal of East China Normal University(Natural Sc, 2019, 2019(3): 63-77.

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