{"type":"ScholarlyArticle","title":"Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation","authors":[{"name":"Guibing Guo","slug":"guibing-guo","given":"Guibing","family":"Guo","affiliation":"Northeastern University , China"},{"name":"Shichang Ouyang","slug":"shichang-ouyang","given":"Shichang","family":"Ouyang","affiliation":"Northeastern University , China"},{"name":"Xiaodong He","slug":"xiaodong-he","given":"Xiaodong","family":"He","affiliation":"JD AI Research , Beijing , China"},{"name":"Fajie Yuan","slug":"fajie-yuan","given":"Fajie","family":"Yuan","affiliation":"Tencent , Shenzhen , China"},{"name":"Xiaohua Liu","slug":"xiaohua-liu","given":"Xiaohua","family":"Liu","affiliation":"JD AI Research , Beijing , China"}],"abstract":"Sequential recommendation systems have become a research hotpot recently to suggest users with the next item of interest (to interact with). However, existing approaches suffer from two limitations: (1) The representation of an item is relatively static and fixed for all users. We argue that even a same item should be represented distinctively with respect to different users and time steps. (2) The generation of a prediction for a user over an item is computed in a single scale (e.g., by their inner product), ignoring the nature of multi-scale user preferences. To resolve these issues, in this paper we propose two enhancing building blocks for sequential recommendation. Specifically, we devise a Dynamic Item Block (DIB) to learn dynamic item representation by aggregating the embeddings of those who rated the same item before that time step. Then, we come up with a Prediction Enhancing Block (PEB) to project user representation into multiple scales, based on which many predictions can be made and attentively aggregated for enhanced learning. Each prediction is generated by a softmax over a sampled itemset rather than the whole item space for efficiency. We conduct a series of experiments on four real datasets, and show that even a basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking accuracy. The code and datasets can be obtained from https://github.com/ouououououou/DIB-PEB-Sequential-RS","doi":"10.24963/ijcai.2019/190","url":"https://ijcai.org/proceedings/2019/190","pdf":"https://ijcai.org/proceedings/2019/0190.pdf","markdown":"https://ijcai.org/proceedings/2019/190.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/190","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Humans and AI","track":"Main track","number":190,"pages":{"first":1373,"last":1379},"keywords":["Humans and AI: Personalization and User Modeling","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2018] Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. Sequential recommendation with user memory net- works. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 108- 116, 2018.","title":"Sequential recommendation with user memory networks","authors":"Hongteng Xu Chen, Yongfeng Xu, Jiaxi Zhang, Yixin Tang, Zheng Cao, Hongyuan Qin, Zha","year":"2018","venue":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Guo et al., 2018] Guibing Guo, Shichang Ouyang, Fajie Yuan, and Xingwei Wang. Approximating word ranking and negative sampling for word embedding. In Proceed- ings of the 28th International Joint Conference on Artifi- cial Intelligence, 2018.","title":"Approximating word ranking and negative sampling for word embedding","authors":"Guo","year":"2018","venue":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/569"},{"position":3,"raw":"[Gutmann and Hyvärinen, 2012] Michael U Gutmann and Aapo Hyvärinen. Noise-contrastive estimation of unnor- malized statistical models, with applications to natural im- age statistics. Journal of Machine Learning Research, 13(Feb):307-361, 2012.","title":"Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics","authors":"Aapo Michael U Gutmann, Hyvärinen","year":"2012","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[He and McAuley, 2016a] Ruining He and Julian McAuley. Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th Interna- tional Conference on Data Mining (ICDM), pages 191- 200, 2016.","title":"Ruining He and Julian McAuley. Fusing similarity models with markov chains for sparse sequential recommendation","authors":"Mcauley He","year":"2016","venue":"IEEE 16th International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He and McAuley, 2016b] Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceed- ings of the 25th international conference on world wide web, pages 507-517, 2016.","title":"Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering","authors":"Mcauley He","year":"2016","venue":"proceedings of the 25th international conference on world wide web","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collab- orative filtering. In Proceedings of the 26th International Conference on World Wide Web, pages 173-182, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939, 2015.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Koren, 2009] Yehuda Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discov- ery and data mining, pages 447-456, 2009.","title":"Collaborative filtering with temporal dynamics","authors":"Koren ;, Yehuda Koren","year":"2009","venue":"Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[McAuley et al., 2015] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. Image-based recommendations on styles and substitutes. In Proceed- ings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 43-52, 2015.","title":"Image-based recommendations on styles and substitutes","authors":"Julian Mcauley, Christopher Targett, Qinfeng Shi, Anton Van Den, Hengel","year":"2015","venue":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pages 452-461, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Rendle et al., 2010] Steffen Rendle, Christoph Freuden- thaler, and Lars Schmidt-Thieme. Factorizing personal- ized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web, pages 811-820, 2010.","title":"Factorizing personalized markov chains for next-basket recommendation","authors":"Rendle","year":"2010","venue":"Proceedings of the 19th international conference on World wide web","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Tang and Wang, 2018] Jiaxi Tang and Ke Wang. Person- alized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Min- ing, pages 565-573, 2018.","title":"Personalized top-n sequential recommendation via convolutional sequence embedding","authors":"Wang ; ] Jiaxi Tang, Ke Tang, Wang","year":"2018","venue":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Yining Wang, 2013] Yuanzhi Li Di He Wei Chen Tie- Yan Liu Yining Wang, Liwei Wang. A theoretical analysis of normalized discounted cumulative gain (ndcg) ranking measures. In Proceedings of the 26th Annual Conference on Learning Theory, 2013.","title":"A theoretical analysis of normalized discounted cumulative gain (ndcg) ranking measures","authors":"Yuanzhi Li, Di He, Wei Chen, Tie-Yan Liu, Yining Wang, Liwei Wang","year":"2013","venue":"Proceedings of the 26th Annual Conference on Learning Theory","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Yuan et al., 2016] Fajie Yuan, Guibing Guo, Joemon M Jose, Long Chen, Haitao Yu, and Weinan Zhang. Lambdafm: learning optimal ranking with factorization machines using lambda surrogates. In Proceedings of the 25th ACM International on Conference on Informa- tion and Knowledge Management, pages 227-236. ACM, 2016.","title":"Lambdafm: learning optimal ranking with factorization machines using lambda surrogates","authors":"Yuan","year":"2016","venue":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Yuan et al., 2019] Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. A simple convolutional generative network for next item rec- ommendation. In Proceedings of the Twelfth ACM Interna- tional Conference on Web Search and Data Mining, pages 582-590. ACM, 2019.","title":"A simple convolutional generative network for next item recommendation","authors":"Yuan","year":"2019","venue":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Zhang et al., 2018] Quangui Zhang, Longbing Cao, Chengzhang Zhu, Zhiqiang Li, and Jinguang Sun. Cou- pledcf: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering. In IJCAI, pages 3662-3668, 2018.","title":"Coupledcf: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering","authors":"Zhang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/509"},{"position":18,"raw":"[Zimdars et al., 2001] Andrew Zimdars, David Maxwell Chickering, and Christopher Meek. Using temporal data for making recommendations. In Proceedings of the Sev- enteenth conference on Uncertainty in artificial intelli- gence, pages 580-588, 2001.","title":"Using temporal data for making recommendations","authors":"Andrew Zimdars, David Maxwell Chickering, Christopher Meek","year":"2001","venue":"Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}