CARE: learning convolutional attentional recurrent embedding for sequential recommendation
Authors: 
Yu-Che Tsai, Cheng-Te LiAuthors Info & Claims
Pages 654 - 660
Abstract
Top-N sequential recommendation is to predict the next few items based on user's sequential interactions with past items. This paper aims at boosting the performance of top-N sequential recommendation based on a state-of-the-art model, Caser. We point out three insufficiencies of Caser - do not model variant-sized sequential patterns, treating the impact of each past time step equally, and cannot learn cumulative features. Then we propose a novel Convolutional Attentional Recurrent Embedding (CARE) learning model. Experiments conducted on a large-scale user-location check-in dataset exhibit promising performance, comparing to Caser.
References
[1]
Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. Where you like to go next: Successive point-of-interest recommendation. In Twenty-Third international joint conference on Artificial Intelligence, 2013.
[2]
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP '14, 2014.
[3]
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference, pages 1459--1468. International World Wide Web Conferences Steering Committee, 2018.
[4]
Aditya Grover and Jure Leskovec. Node2vec: Scalable feature learning for networks. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, pages 855--864, 2016.
[5]
Ruining He and Julian McAuley. Fusing similarity models with markov chains for sparse sequential recommendation. 2016 IEEE 16th International Conference on Data Mining), pages 191--200, 2016.
[6]
Balazs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks, 2015.
[7]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939, 2015.
[8]
Hsun-Ping Hsieh, Cheng-Te Li, and Shou-De Lin. Measuring and recommending time-sensitive routes from location-based data. ACM Trans. Intell. Syst. Technol., 5(3):45:1--45:27, 2014.
[9]
Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 263--272, 2008.
[10]
Zhao Kang, Chong Peng, and Qiang Cheng. Top-n recommender system via matrix completion. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, pages 179--184, 2016.
[11]
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2014.
[12]
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[13]
Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, (8):30--37, 2009.
[14]
Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412--1421, 2015.
[15]
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems, pages 130--137. ACM, 2017.
[16]
Steffen Rendle, Christoph Freudenthaler, 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, UAI '09, pages 452--461, 2009.
[17]
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web, pages 811--820. ACM, 2010.
[18]
Badrul Munir Sarwar, George Karypis, Joseph A Konstan, John Riedl, et al. Item-based collaborative filtering recommendation algorithms. Www, 1:285--295, 2001.
[19]
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pages 555--563. ACM, 2019.
[20]
Jiaxi Tang and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM '18, pages 565--573, 2018.
Index Terms
- CARE: learning convolutional attentional recurrent embedding for sequential recommendation
Index terms have been assigned to the content through auto-classification.
Recommendations
News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender SystemsNews recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
CHAMELEON: a deep learning meta-architecture for news recommender systems
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsNews recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
Exploiting User and Item Attributes for Sequential Recommendation
Neural Information ProcessingAbstractThis paper exploits both the user and item attribute information for sequential recommendation. Attribute information has been explored in a number of traditional recommendation systems and proved to be effective to enhance the recommend ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2021
693 pages
ISBN:9781450391283
DOI:10.1145/3487351
- Editors:
- Michele Coscia,
- Alfredo Cuzzocrea,
- Kai Shu,
- General Chairs:
- Ralf Klamma,
- Sharyn O'Halloran,
- Jon Rokne
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
In-Cooperation
- IEEE CS
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 January 2022
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
ASONAM '21
Sponsor:
ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining
November 8 - 11, 2021
Virtual Event, Netherlands
Acceptance Rates
ASONAM '21 Paper Acceptance Rate 22 of 118 submissions, 19%;
Overall Acceptance Rate 116 of 549 submissions, 21%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 56Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)1
Reflects downloads up to 30 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in