Abstract
Thibaut Thonet, Jean-Michel Renders, Mario Choi, Jinho Kim |
44th European Conference on Information Retrieval (ECIR), Stavanger, Norway, 10-14 April, 2022 |
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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.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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