MM-GEF: Multi-modal representation meet collaborative filtering
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914912516702208 |
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| author | Wu, Hao Ariza-Casabona, Alejandro Twardowski, Bartłomiej Wijaya, Tri Kurniawan |
| author_facet | Wu, Hao Ariza-Casabona, Alejandro Twardowski, Bartłomiej Wijaya, Tri Kurniawan |
| contents | In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07222 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MM-GEF: Multi-modal representation meet collaborative filtering Wu, Hao Ariza-Casabona, Alejandro Twardowski, Bartłomiej Wijaya, Tri Kurniawan Information Retrieval Artificial Intelligence In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods. |
| title | MM-GEF: Multi-modal representation meet collaborative filtering |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2308.07222 |