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.

翻译：序列推荐系统旨在根据用户的历史交互预测其下一个感兴趣的项目。然而，一个长期存在的问题是如何区分用户的长期与短期兴趣——这两类兴趣可能具有异质性，且对后续推荐的影响程度不同。现有方法通常通过穷举搜索或经验性设定来预先确定短期兴趣长度，这不仅效率低下，且往往效果欠佳。近期先进的基于Transformer的模型虽能实现最优性能，但其计算复杂度与输入序列长度呈二次方关系。为此，本文提出一种新型序列推荐系统AutoMLP，旨在通过用户历史交互更精准地建模其长短期兴趣。此外，我们设计了一种通过端到端优化自动且自适应地搜索最优短期兴趣长度的算法。大量实验表明，AutoMLP在保持线性计算复杂度的同时，其性能可媲美当前最优方法。