{"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/mining-latent-structures-for-multimedia","title":"Mining Latent Structures for Multimedia Recommendation","arxiv_id":"2104.09036","date":"2021-04-19","proceeding":null,"authors":["Jinghao Zhang","Yanqiao Zhu","Qiang Liu","Shu Wu","Shuhui Wang","Liang Wang"],"abstract":"Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and further boosting recommendation. To this end, we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations. These enriched item representations can then be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and validate the efficacy of mining latent item-item relationships from multimodal features.","url_abs":"https://arxiv.org/abs/2104.09036v2","url_pdf":"https://arxiv.org/pdf/2104.09036v2.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":"mining-latent-structures-for-multimedia","repo_url":"https://github.com/CRIPAC-DIG/LATTICE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"multi-modal-recommendation","task_name":"Multi-modal Recommendation"},{"task_slug":"multimedia-recommendation","task_name":"Multimedia recommendation"},{"task_slug":"multimodal-recommendation","task_name":"Multimodal Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-modal-recommendation-on-amazon-baby","task":"Multi-modal Recommendation","dataset":"Amazon Baby","model":"LATTICE","rank_in_archive_order":3,"of":10,"metrics":{"NDCG@20":"0.0370"},"uses_additional_data":false},{"leaderboard":"/sota/multi-modal-recommendation-on-amazon-clothing","task":"Multi-modal Recommendation","dataset":"Amazon Clothing","model":"LATTICE","rank_in_archive_order":3,"of":10,"metrics":{"NDCG@20":"0.0330"},"uses_additional_data":false},{"leaderboard":"/sota/multi-modal-recommendation-on-amazon-sports","task":"Multi-modal Recommendation","dataset":"Amazon Sports","model":"LATTICE","rank_in_archive_order":5,"of":10,"metrics":{"NGCG@20":"0.0421"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.09036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.09036"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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