Long-tail Session-based Recommendation

• URL: http://arxiv.org/abs/2007.12329v2
• Date: Tue, 4 Aug 2020 02:00:35 GMT
• Title: Long-tail Session-based Recommendation
• Authors: Siyi Liu and Yujia Zheng
• Abstract summary: We propose a novel network architecture, namely TailNet, to improve long-tail recommendation performance. A novel is proposed and applied in TailNet to determine user preference between two types of items, so as to softly adjust and personalize recommendations.
• Score: 7.832914615902803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data. However, none of the existing session-based recommendation methods explicitly takes the long-tail recommendation into consideration, which plays an important role in improving the diversity of recommendation and producing the serendipity. As the distribution of items with long-tail is prevalent in session-based recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), more attention should be put on the long-tail session-based recommendation. In this paper, we propose a novel network architecture, namely TailNet, to improve long-tail recommendation performance, while maintaining competitive accuracy performance compared with other methods. We start by classifying items into short-head (popular) and long-tail (niche) items based on click frequency. Then a novel is proposed and applied in TailNet to determine user preference between two types of items, so as to softly adjust and personalize recommendations. Extensive experiments on two real-world datasets verify the superiority of our method compared with state-of-the-art works.

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