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Multi-intent-aware Session-based Recommendationoa mark
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Publication Year
2024-07-10
Journal
SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
Association for Computing Machinery, Inc
Citation
SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.2532-2536
Keyword
multiple intentssession-based recommendation
Mesh Keyword
EmbeddingsLearn+Multiple intentMultiple sessionsPerformanceSession-based recommendationState of the art
All Science Classification Codes (ASJC)
Information SystemsSoftware
Abstract
Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session. Most existing SBR models focus on designing sophisticated neural-based encoders to learn a session representation, capturing the relationship among session items. However, they tend to focus on the last item, neglecting diverse user intents that may exist within a session. This limitation leads to significant performance drops, especially for longer sessions. To address this issue, we propose a novel SBR model, called Multi-intent-aware Session-based Recommendation Model (MiaSRec). It adopts frequency embedding vectors indicating the item frequency in session to enhance the information about repeated items. MiaSRec represents various user intents by deriving multiple session representations centered on each item and dynamically selecting the important ones. Extensive experimental results show that MiaSRec outperforms existing state-of-the-art SBR models on six datasets, particularly those with longer average session length, achieving up to 6.27% and 24.56% gains for MRR@20 and Recall@20. Our code is available at https://github.com/jin530/MiaSRec.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37153
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200553627&origin=inward
DOI
https://doi.org/10.1145/3626772.3657928
Journal URL
http://dl.acm.org/citation.cfm?id=3626772
Type
Conference
Funding
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019-0-00421, 2022-0-00680-003, IITP-2024-2020-0-01821, and NRF-2018R1A5A1060031).
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Cho, Hyunsouk Image
Cho, Hyunsouk조현석
Department of Software and Computer Engineering
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