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Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback by Xu, Yuanbo ;Yang, Yongjian ;Wang, En ;Han, Jiayu ;Zhuang, Fuzhen ;Yu, Zhiwen ;Xiong, Hui is a Computer Science article available to read on EtoBox.

What is Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback about?

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity , which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accur

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Author

Xu, Yuanbo ;Yang, Yongjian ;Wang, En ;Han, Jiayu ;Zhuang, Fuzhen ;Yu, Zhiwen ;Xiong, Hui

Publisher

Association for Computing Machinery (ACM)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)