Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
It addresses the lack of systematic study in designing effective deep learning models for sequential recommendation, which is incremental as it synthesizes existing work.
This survey systematically reviews deep learning methods for sequential recommendation, categorizing algorithms by behavioral sequence types and evaluating key performance factors to guide future research.
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.