Multimodal Recommender Systems: A Survey
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929485272580096 |
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| author | Liu, Qidong Hu, Jiaxi Xiao, Yutian Zhao, Xiangyu Gao, Jingtong Wang, Wanyu Li, Qing Tang, Jiliang |
| author_facet | Liu, Qidong Hu, Jiaxi Xiao, Yutian Zhao, Xiangyu Gao, Jingtong Wang, Wanyu Li, Qing Tang, Jiliang |
| contents | The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short videos, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to four categories, i.e., Modality Encoder, Feature Interaction, Feature Enhancement and Model Optimization. Besides, to make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper. To access more details of the surveyed papers, such as implementation code, we open source a repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_03883 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multimodal Recommender Systems: A Survey Liu, Qidong Hu, Jiaxi Xiao, Yutian Zhao, Xiangyu Gao, Jingtong Wang, Wanyu Li, Qing Tang, Jiliang Information Retrieval Artificial Intelligence The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short videos, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to four categories, i.e., Modality Encoder, Feature Interaction, Feature Enhancement and Model Optimization. Besides, to make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper. To access more details of the surveyed papers, such as implementation code, we open source a repository. |
| title | Multimodal Recommender Systems: A Survey |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2302.03883 |