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.
Abstract (translated)
Sequential recommender systems旨在根据用户的历史交互预测他们感兴趣的下一件物品。然而,一个长期存在的问题是如何区分用户的长期兴趣和短期兴趣,这些兴趣可能不同并且为下一份推荐贡献不同。现有的方法通常通过搜索或经验来设置预定义的短期兴趣长度,这种方法要么非常低效,要么产生较差的结果。最近的先进的Transformer模型尽管克服了上述问题,但仍然表现出最先进的性能,但它们输入序列的长度具有quadratic的计算复杂度。因此,本文提出了一种新型的Sequential recommender system,名为AutoMLP,旨在更好地从用户的历史交互中建模长期兴趣和短期兴趣。此外,我们设计了一种自动化和自适应的搜索算法,通过端到端优化,以选择更好的短期兴趣长度。通过广泛的实验,我们表明AutoMLP与最先进的方法相比具有竞争力性能,同时保持线性计算复杂度。
URL
https://arxiv.org/abs/2303.06337
