In the field of sequential recommendation, deep learning--(DL) based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically, we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequences, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to showcase and demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.

中文翻译：

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用于顺序推荐的深度学习

在顺序推荐领域，基于深度学习（DL）的方法在过去几年中受到了很多关注，并超越了基于马尔可夫链和基于因子分解的传统模型。然而，关于基于 DL 的方法的系统研究很少，特别是关于如何设计一个有效的 DL 模型进行顺序推荐。在这种观点下，本次调查通过考虑上述问题，重点关注基于 DL 的顺序推荐系统。具体来说，我们阐述了顺序推荐的概念，提出了对现有算法的三种行为序列的分类，总结了影响基于深度学习的模型性能的关键因素，并进行了相应的评估，以展示和证明这些行为序列的效果。因素。