Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data

doi:10.13190/j.jbupt.2020-229

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 106-111.doi: 10.13190/j.jbupt.2020-229

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Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data

JI Zhen-yan¹, WU Meng-dan^1,2, YANG Chun¹, LI Jun-dong¹

1. School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Received:2020-11-12 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: Social relationship plays an important role in life, and users are often affected by their friends' preferences. It is easier for users to choose items that their friends have purchased. In order to solve the cold start problem of the recommended system, a recommendation system that integrates social relationships is studied, and Bayesian personalized ranking review score social model and scalable Bayesian personalized ranking review score social model are proposed. The proposed fusion recommendation models integrate multi-source heterogeneous data such as scores, reviews, and social relationships from the data source level, introduce social relationships into the recommendation system through the user friend trust model, use the paragraph vector-distributed memory model to process review, use the fully connected neural network to process rating, and use an improved Bayesian personalized ranking model to optimize the ranking results. Experiments are conducted on the Yelp public dataset. It is shown that the recommendation accuracy of the two proposed models are better than other recommendation models.

Key words: recommendation system, multi-source heterogeneous data, social relation, hybrid model, data fusion

CLC Number:

TP391

JI Zhen-yan, WU Meng-dan, YANG Chun, LI Jun-dong. Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 106-111.

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Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data

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