%0 Generic %1 fang2019learning %A Fang, Hui %A Zhang, Danning %A Shu, Yiheng %A Guo, Guibing %D 2019 %K basket recommendation sequential survey %T Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations %U http://arxiv.org/abs/1905.01997 %X 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.

@misc{fang2019learning, 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.}, added-at = {2020-12-21T14:11:08.000+0100}, author = {Fang, Hui and Zhang, Danning and Shu, Yiheng and Guo, Guibing}, biburl = {https://www.bibsonomy.org/bibtex/26517a8d9ec41cbc552446f064de9c34a/nosebrain}, description = {[1905.01997] Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations}, interhash = {3c0e64182e3ba00e730cf538b79bde58}, intrahash = {6517a8d9ec41cbc552446f064de9c34a}, keywords = {basket recommendation sequential survey}, note = {cite arxiv:1905.01997Comment: 41 pages, 19 figures, 6 tables, 155 references, TOIS accepted}, timestamp = {2020-12-21T14:11:08.000+0100}, title = {Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations}, url = {http://arxiv.org/abs/1905.01997}, year = 2019 }