Hierarchical Latent Relation Modeling for Collaborative Metric Learning
- URL: http://arxiv.org/abs/2108.04655v1
- Date: Mon, 26 Jul 2021 17:45:11 GMT
- Title: Hierarchical Latent Relation Modeling for Collaborative Metric Learning
- Authors: Viet-Anh Tran and Guillaume Salha-Galvan and Romain Hennequin and
Manuel Moussallam
- Abstract summary: Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering.
We present a hierarchical CML model that jointly captures latent user-item and item-item relations from implicit data.
We empirically show the relevance of this joint relational modeling, by outperforming existing CML models on recommendation tasks on several real-world datasets.
- Score: 8.59872983871365
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Collaborative Metric Learning (CML) recently emerged as a powerful paradigm
for recommendation based on implicit feedback collaborative filtering. However,
standard CML methods learn fixed user and item representations, which fails to
capture the complex interests of users. Existing extensions of CML also either
ignore the heterogeneity of user-item relations, i.e. that a user can
simultaneously like very different items, or the latent item-item relations,
i.e. that a user's preference for an item depends, not only on its intrinsic
characteristics, but also on items they previously interacted with. In this
paper, we present a hierarchical CML model that jointly captures latent
user-item and item-item relations from implicit data. Our approach is inspired
by translation mechanisms from knowledge graph embedding and leverages
memory-based attention networks. We empirically show the relevance of this
joint relational modeling, by outperforming existing CML models on
recommendation tasks on several real-world datasets. Our experiments also
emphasize the limits of current CML relational models on very sparse datasets.
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