DMRAN: a hierarchical fine-grained attention-based network for recommendation

Huizhao Wang, Guanfeng Liu, An Liu*, Zhixu Li, Kai Zheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

8 Citations (Scopus)

Abstract

The conventional methods for the next-item recommendation are generally based on RNN or one-dimensional attention with time encoding. They are either hard to preserve the long-term dependencies between different interactions, or hard to capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i.e., Item level Attention and Feature Level Self-attention, which are to pick out the most relevant items from the sequence of user's historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the fine-grained user preferences to better support the next-item recommendation. Extensive experiments on two real-world datasets illustrate that DMRAN can improve the efficiency and effectiveness of the recommendation compared with the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
Place of PublicationFreiburg, Germany
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3698-3704
Number of pages7
ISBN (Electronic)9780999241141
DOIs
Publication statusPublished - 2019
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2019-August
ISSN (Print)1045-0823

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
CountryChina
CityMacao
Period10/08/1916/08/19

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