IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

Jun Wu, Xinyu Song, Xiaxia Niu, Li Shi, Lu Gao, Liping Geng, Dan Wang and Dongkui Zhang
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Jun Wu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Xinyu Song: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Xiaxia Niu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Li Shi: China Information Communication Technology Group Corporation, Beijing 100191, China
Lu Gao: China Information Communication Technology Group Corporation, Beijing 100191, China
Liping Geng: Datang Carera (Beijing) Investment Co., Ltd., Beijing 100191, China
Dan Wang: Datang Carera (Beijing) Investment Co., Ltd., Beijing 100191, China
Dongkui Zhang: Datang Carera (Beijing) Investment Co., Ltd., Beijing 100191, China

Mathematics, 2022, vol. 10, issue 14, 1-16

Abstract: It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations.

Keywords: community e-commerce; recommendation system; probabilistic matrix factorization; implicit trust; fuzzy c-means clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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