Abstract
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 to 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 sequence, summarize the key
factors affecting the performance of DL-based models, and conduct corresponding
evaluations to demonstrate the effects of these factors. We conclude this
survey by systematically outlining future directions and challenges in this
field.
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