AutoMLP: Automated MLP for Sequential Recommendations

• URL: http://arxiv.org/abs/2303.06337v1
• Date: Sat, 11 Mar 2023 07:50:49 GMT
• Title: AutoMLP: Automated MLP for Sequential Recommendations
• Authors: Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo
• Abstract summary: Sequential recommender systems aim to predict users' next interested item given their historical interactions. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience. This paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests.
• Score: 20.73096302505791
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.

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