Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization

Jianshan Sun (), Jian Song (), Yuanchun Jiang (), Yezheng Liu () and Jun Li ()
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Jianshan Sun: Hefei University of Technology, School of Management
Jian Song: Hefei University of Technology, School of Management
Yuanchun Jiang: Hefei University of Technology, School of Management
Yezheng Liu: Hefei University of Technology, School of Management
Jun Li: Hefei University of Technology, School of Economics

Electronic Markets, 2022, vol. 32, issue 1, No 7, 121 pages

Abstract: Abstract Recommender systems have been an important tool to filter and tailor the best content for online users. Classical recommender system methods typically face the filter bubble problem where users effectively get isolated from a diversity of viewpoints or content. How to provide relevant and diversified goods for online users has become a challenging problem. In this study, we develop a cross-domain matrix factorization model based on adaptive diversity regularization to address the above challenges. We leverage collective MF model to transfer users’ rating pattern, utilize social tags to transfer semantic information between domains, and design a novel adaptive diversity regularization to improve recommendation performance. Comprehensive experiments on real cross-domain datasets demonstrate the effectiveness of our model. Results show that our model can achieve a decent balance between recommendation accuracy and diversity, and the recommendation polarity can also be alleviated.

Keywords: Filter bubble; Cross-domain recommendation system; Matrix factorization; Recommendation diversity (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12525-021-00492-1

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