{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-modality-is-all-you-need-for","title":"Multi-Modality is All You Need for Transferable Recommender Systems","arxiv_id":"2312.09602","date":"2023-12-15","proceeding":null,"authors":["Youhua Li","Hanwen Du","Yongxin Ni","Pengpeng Zhao","Qi Guo","Fajie Yuan","Xiaofang Zhou"],"abstract":"ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developing transferable RecSys and is also susceptible to the cold-start issue. In this paper, we unleash the boundaries of the ID-based paradigm and propose a Pure Multi-Modality based Recommender system (PMMRec), which relies solely on the multi-modal contents of the items (e.g., texts and images) and learns transition patterns general enough to transfer across domains and platforms. Specifically, we design a plug-and-play framework architecture consisting of multi-modal item encoders, a fusion module, and a user encoder. To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders. To ensure the robustness of user representations, we propose a novel noised item detection objective and a robustness-aware contrastive learning objective, which work together to denoise user sequences in a self-supervised manner. PMMRec is designed to be loosely coupled, so after being pre-trained on the source data, each component can be transferred alone, or in conjunction with other components, allowing PMMRec to achieve versatility under both multi-modality and single-modality transfer learning settings. Extensive experiments on 4 sources and 10 target datasets demonstrate that PMMRec surpasses the state-of-the-art recommenders in both recommendation performance and transferability. Our code and dataset is available at: https://github.com/ICDE24/PMMRec.","url_abs":"https://arxiv.org/abs/2312.09602v2","url_pdf":"https://arxiv.org/pdf/2312.09602v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-modality-is-all-you-need-for","repo_url":"https://github.com/icde24/pmmrec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.09602","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}