Journal
ACM TRANSACTIONS ON INFORMATION SYSTEMS
Volume 39, Issue 1, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3426723
Keywords
Sequential recommendation; session-based recommendation; deep learning; influential factors; survey; evaluations
Categories
Funding
- National Natural Science Foundation of China [71601104, 71601116, 71771141, 61972078, 61702084]
- Fundamental Research Funds for the Central Universities [N181705007]
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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.
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