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- W3105472188 endingPage "42" @default.
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- W3105472188 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 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." @default.
- W3105472188 created "2020-11-23" @default.
- W3105472188 creator A5007061198 @default.
- W3105472188 creator A5009445787 @default.
- W3105472188 creator A5046565976 @default.
- W3105472188 creator A5056930150 @default.
- W3105472188 date "2020-11-13" @default.
- W3105472188 modified "2023-10-12" @default.
- W3105472188 title "Deep Learning for Sequential Recommendation" @default.
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