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.

提出了一种新型的、自适应的持续推荐系统 AutoMLP，旨在更好地对用户的长期和短期兴趣进行建模，通过端到端优化进行自动化和自适应搜索算法，与最先进的方法具有相当竞争性的性能，同时保持线性计算复杂度。