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LLMs have emerged as a promising approach by organizing these features into prompts and fine-tuning LLMs to predict the interaction label. However, our feature-wise and interaction-wise analysis reveals two critical limitations, leading to incomplete relationship modeling between user\u2013item features and interaction labels: (i) LLMs tend to utilize only a subset of available features, neglecting others, and (ii) they struggle with predictions for tail items that appear less frequently in the training samples. To bridge this gap, we propose Feature-Instructed Large language model for Monitoring (\n            <jats:monospace>FILM<\/jats:monospace>\n            ), which introduces a self-monitoring temperature mechanism that dynamically guides LLMs to focus on informative features, and an auxiliary compaction loss that facilitates better feature-interaction relationship learning for tail items. By integrating these two designs,\n            <jats:monospace>FILM<\/jats:monospace>\n            not only improves feature utilization in LLMs but also enhances predictions for tail items. Furthermore, we demonstrate that\n            <jats:monospace>FILM<\/jats:monospace>\n            -generated interaction embeddings can be transferred to lightweight models, enabling efficient deployment. Extensive experiments demonstrate that\n            <jats:monospace>FILM<\/jats:monospace>\n            achieves significant performance improvements over state-of-the-art baselines by learning better relationships between user\u2013item features and interaction labels and generalizes under different LLM backbones.\n          <\/jats:p>","DOI":"10.1145\/3763789","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T15:08:06Z","timestamp":1756220886000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-Monitoring Large Language Models for Click-Through Rate Prediction"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8301-8470","authenticated-orcid":false,"given":"Huachi","family":"Zhou","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0514-6700","authenticated-orcid":false,"given":"Kaijing","family":"Yu","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0247-4942","authenticated-orcid":false,"given":"Qinggang","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6816-5344","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"City University of Macau, Taipa, Macao"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6677-7504","authenticated-orcid":false,"given":"Daochen","family":"Zha","sequence":"additional","affiliation":[{"name":"Rice University, Houston, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7638-8564","authenticated-orcid":false,"given":"Wenqi","family":"Pei","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8918-5616","authenticated-orcid":false,"given":"Anthony","family":"Kong","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3867-900X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"volume-title":"International Conference on Learning Representations","author":"Abdelfattah Mohamed S.","key":"e_1_3_2_2_2","unstructured":"Mohamed S. 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