%0 Journal Article %1 fang2019overview-sequ-recommendation %A Fang, Hui %A Zhang, Danning %A Shu, Yiheng %A Guo, Guibing %D 2019 %J ACM Transactions on Information Systems %K thema:Visualbert(Sequential) %N 1 %P 1-42 %R 10.1145/3423044 %T Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations %U https://dl.acm.org/doi/abs/10.1145/3426723 %V 39 %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.

@article{fang2019overview-sequ-recommendation, 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 = {2021-06-05T13:22:23.000+0200}, author = {Fang, Hui and Zhang, Danning and Shu, Yiheng and Guo, Guibing}, biburl = {https://www.bibsonomy.org/bibtex/252c902c169071b8684f7e438c8a8365a/jan_seidlein}, description = {Sequential Recommendation: Summary of Stat-of-the-Art Methods}, doi = {10.1145/3423044}, interhash = {3c0e64182e3ba00e730cf538b79bde58}, intrahash = {52c902c169071b8684f7e438c8a8365a}, issn = {1046-8188}, journal = {ACM Transactions on Information Systems}, keywords = {thema:Visualbert(Sequential)}, month = jan, note = {cite arxiv:1905.01997Comment: 41 pages, 19 figures, 6 tables, 155 references, TOIS accepted}, number = 1, pages = {1-42}, timestamp = {2021-06-05T13:22:23.000+0200}, title = {Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations}, url = {https://dl.acm.org/doi/abs/10.1145/3426723}, volume = 39, year = 2019 }