Article

Joint Personalized Search and Recommendation with Hypergraph Convolutional Networks

Authors:

Thibaut Thonet,

Jean-Michel Renders,

Jinho KimAuthors Info & Claims

Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part I

Pages 443 - 456

https://doi.org/10.1007/978-3-030-99736-6_30

Published: 10 April 2022 Publication History

Abstract

Traditionally, the search and recommendation tasks are performed separately, by distinct models. Having a unique model for the two tasks is however particularly appealing for platforms that offer search and recommendation services to a shared user base over common items. In this paper, we study this unification scenario denoted as Joint Personalized Search and Recommendation (JPSR). To tackle this problem, we introduce HyperSaR, an hypergraph convolutional approach for search and recommendation. From the interaction data, we first build an hypergraph composed of user, item and query keyword nodes in which recommendation instances form user-item edges and search instances define user-item-query hyperedges. We then propagate user, item and query keyword embeddings using hypergraph convolution, and train HyperSaR with the combination of two complementary losses. The first one amounts to assessing the probability of an interaction, while the second one aims at predicting the query of a search interaction given a (user, item) pair. The proposed method is evaluated on the JPSR task using three datasets: a real-world, industrial dataset, and the public MovieLens and Lastfm datasets, which have been adapted to the task. Our experiments demonstrate the superior effectiveness of HyperSaR over competing approaches.

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Cited By

Boratto LMalitesta DMarras MMedda GMusto CPurificato E(2024)First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024)Advances in Information Retrieval10.1007/978-3-031-56069-9_56(415-421)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_56
Rostami MVardasbi AAliannejadi MOussalah M(2024)Emotional Insights for Food RecommendationsAdvances in Information Retrieval10.1007/978-3-031-56060-6_16(238-253)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_16
Antelmi ACordasco GPolato MScarano VSpagnuolo CYang D(2023)A Survey on Hypergraph Representation LearningACM Computing Surveys10.1145/360577656:1(1-38)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605776

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cover image Guide Proceedings

Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part I

Apr 2022

733 pages

ISBN:978-3-030-99735-9

DOI:10.1007/978-3-030-99736-6

Editors:
Matthias Hagen
Martin Luther University Halle-Wittenberg, Halle, Germany
,
Suzan Verberne
Leiden University, Leiden, The Netherlands
,
Craig Macdonald
University of Glasgow, Glasgow, UK
,
Christin Seifert
University of Duisburg-Essen, Essen, Germany
,
Krisztian Balog
University of Stavanger, Stavanger, Norway
,
Kjetil Nørvåg
Norwegian University of Science and Technology, Trondheim, Norway
,
Vinay Setty
University of Stavanger, Stavanger, Norway

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 10 April 2022

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Cited By

Boratto LMalitesta DMarras MMedda GMusto CPurificato E(2024)First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024)Advances in Information Retrieval10.1007/978-3-031-56069-9_56(415-421)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_56
Rostami MVardasbi AAliannejadi MOussalah M(2024)Emotional Insights for Food RecommendationsAdvances in Information Retrieval10.1007/978-3-031-56060-6_16(238-253)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_16
Antelmi ACordasco GPolato MScarano VSpagnuolo CYang D(2023)A Survey on Hypergraph Representation LearningACM Computing Surveys10.1145/360577656:1(1-38)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605776

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