End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Name
3589335.3651499.pdf
Size
891.48 KB
Format
Adobe PDF
Checksum (MD5)
71c18a35097834b8b5168cf199764bfe
Author(s)
Baikalov, Vladimir
Frolov, Evgeny
Date Issued
May 13, 2024
Publisher
ACM|Companion Proceedings of the ACM Web Conference 2024
Citation
Baikalov, Vladimir and Frolov, Evgeny. 2024. "End-to-End Graph-Sequential Representation Learning for Accurate Recommendations."
Version
Final published version
Abstract
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within
behavioral data, leading to promising outcomes in personalized
ranking and next-item recommendation tasks while maintaining
good scalability. However, they capture very different signals from
data. While the former approach represents users directly through
ordered interactions with recent items, the latter aims to capture
indirect dependencies across the interactions graph. This paper
presents a novel multi-representational learning framework exploiting these two paradigms’ synergies. Our empirical evaluation
on several datasets demonstrates that mutual training of sequential
and graph components with the proposed framework significantly
improves recommendations performance.
Description
WWW ’24 Companion, May 13–17, 2024, Singapore, Singapore
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3589335.3651499
