{"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/2505-10940","title":"Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation","arxiv_id":"2505.10940","date":"2025-05-16","proceeding":null,"authors":["Qing Yu","Xiaobei Wang","Shuchang Liu","Yandong Bai","Xiaoyu Yang","Xueliang Wang","Chang Meng","Shanshan Wu","Hailan Yang","Huihui Xiao","Xiang Li","Fan Yang","Xiaoqiang Feng","Lantao Hu","Han Li","Kun Gai","Lixin Zou"],"abstract":"Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank paradigm, which focus on discovering and modeling item topics (e.g., categories), and capturing user preferences on these topics based on historical interactions. However, this paradigm often neglects the modeling of user characteristics and their social roles, which are logical confounders influencing the correlated interest and user preference transition. To bridge this gap, we introduce the user role identification task and the behavioral logic modeling task that aim to explicitly model user roles and learn the logical relations between item topics and user social roles. We show that it is possible to explicitly solve these tasks through an efficient integration framework of Large Language Model (LLM) and recommendation systems, for which we propose TagCF. On the one hand, TagCF exploits the (Multi-modal) LLM's world knowledge and logic inference ability to extract realistic tag-based virtual logic graphs that reveal dynamic and expressive knowledge of users, refining our understanding of user behaviors. On the other hand, TagCF presents empirically effective integration modules that take advantage of the extracted tag-logic information, augmenting the recommendation performance. We conduct both online experiments and offline experiments with industrial and public datasets as verification of TagCF's effectiveness, and we empirically show that the user role modeling strategy is potentially a better choice than the modeling of item topics. Additionally, we provide evidence that the extracted logic graphs are empirically a general and transferable knowledge that can benefit a wide range of recommendation tasks.","url_abs":"https://arxiv.org/abs/2505.10940v2","url_pdf":"https://arxiv.org/pdf/2505.10940v2.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":[],"tasks":[{"task_slug":"general-knowledge","task_name":"General Knowledge"},{"task_slug":"large-language-model","task_name":"Large Language Model"},{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"tag","task_name":"TAG"},{"task_slug":"world-knowledge","task_name":"World Knowledge"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2505.10940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.10940"}},"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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