Next-item recommendations in short sessions

Song, W; Wang, S; Wang, Y; Wang, S

Next-item recommendations in short sessions

Song, W Wang, S

Wang, Y Wang, S

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Conference Proceeding
Citation:: RecSys 2021 - 15th ACM Conference on Recommender Systems, 2021, pp. 282-291
Issue Date:: 2021-09-13

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Field	Value	Language
dc.contributor.author	Song, W
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Wang, Y
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.date	2021-09-27
dc.date.accessioned	2022-07-24T22:54:49Z
dc.date.available	2022-07-24T22:54:49Z
dc.date.issued	2021-09-13
dc.identifier.citation	RecSys 2021 - 15th ACM Conference on Recommender Systems, 2021, pp. 282-291
dc.identifier.isbn	9781450384582
dc.identifier.uri	http://hdl.handle.net/10453/159147
dc.description.abstract	The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborativeRecommender neTwork (INSERT) for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.
dc.language	en
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	RecSys 2021 - 15th ACM Conference on Recommender Systems
dc.relation.ispartof	15th ACM Conference on Recommender Systems (RECSYS)
dc.relation.isbasedon	10.1145/3460231.3474238
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Next-item recommendations in short sessions
dc.type	Conference Proceeding
utslib.location.activity	Amsterdam, NETHERLANDS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-24T22:54:46Z
pubs.finish-date	2021-10-01
pubs.publication-status	Published
pubs.start-date	2021-09-27

Abstract:

The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborativeRecommender neTwork (INSERT) for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.

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http://hdl.handle.net/10453/159147