Recurrent Collaborative Filtering for unifying general and sequential recommender

Disheng Dong, Xiaolin Zheng, Ruixun Zhang, Yan Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

11 Citations (Scopus)

Abstract

General recommender and sequential recommender are two commonly applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors, whereas sequential recommender focuses on exploring the item-to-item sequential relations, failing to model the global user preferences. In addition, better recommendation performance has recently been achieved by adopting an approach to combining them. However, the existing approaches are unable to solve both tasks in a unified way and cannot capture the whole historical sequential information. In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model.Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other. Furthermore, we empirically demonstrate on MovieLens and Netflix datasets that our model outperforms the state-of-the-art methods across the tasks of both sequential and general recommender.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
EditorsJérôme Lang
Place of PublicationCalifornia
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3350-3356
Number of pages7
ISBN (Electronic)9780999241127
DOIs
Publication statusPublished - 2018
Event27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, IJCAI-ECAI 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018

Conference

Conference27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, IJCAI-ECAI 2018
CountrySweden
CityStockholm
Period13/07/1819/07/18

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  • Cite this

    Dong, D., Zheng, X., Zhang, R., & Wang, Y. (2018). Recurrent Collaborative Filtering for unifying general and sequential recommender. In J. Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) (pp. 3350-3356). California: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/465