A Hybrid Fuzzy Profiling-Nonnegative Latent Factor Model Considering Consumer Preference at Different Levels

Symmetry

Jing Wang¹  Jianqiang Wang²  Yu Li² 

[1] College of Tourism, Hunan Normal University, Changsha 410081, China;School of Business, Central South University, Changsha 410083, China;

关键词: implicit feedback information;    consumer preference;    fuzzy set;    latent factor model;    recommender system;   

DOI  :  10.3390/sym12091399

来源: DOAJ

【 摘 要 】

The implicit feedback information associated with actual behavior can symmetrically reflect consumer preference, which is valuable and deserves a deep investigation. Considering the inherent uncertainty and fuzziness of implicit data, the question of how to depict consumer preference in such data is always a critical and difficult issue. In this paper, a fuzzy profiling-nonnegative latent factor (FP-NLF) model is proposed to address this problem. First, a fuzzy profiling (FP) procedure is designed for characterization of consumer preference at various levels, where the fuzzy set is introduced to manage the uncertainty and fuzziness of implicit data. Two series of strategies are provided to determine the comprehensive preference for different scenarios and commercial intentions. Subsequently, a nonnegative latent factor (NLF) model is adopted to make predictions in the case of notably sparse data. Finally, a higher quality recommendation is ultimately produced for which only the products satisfying given preference level are recommended. In addition, a case study with real data is conducted for a detailed demonstration, and the results reveal the feasibility and superiority of our proposal through comparative analysis. Finally, the sensitivity analysis explores the influences of changing weights of strategy, which can provide guidance for developing purposeful strategies to better serve consumers.

【 授权许可】

Unknown