Every Preference Has Its Strength: Injecting Ordinal Semantics into LLM-Based Recommenders

• URL: http://arxiv.org/abs/2605.10323v1
• Date: Mon, 11 May 2026 10:21:36 GMT
• Title: Every Preference Has Its Strength: Injecting Ordinal Semantics into LLM-Based Recommenders
• Abstract summary: We propose Ordinal Semantic Anchoring (OSA), a hybrid CF-LLM framework that explicitly incorporates preference strength by modeling interaction-level user feedback.<n> Experiments on multiple real-world datasets demonstrate that OSA consistently outperforms existing baselines.
• Score: 5.600161718503259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work has shown that large language models (LLMs) can enhance recommender systems by integrating collaborative filtering (CF) signals through hybrid prompting. However, most existing CF-LLM frameworks collapse explicit ratings into implicit or positive-only feedback, discarding the ordinal structure that conveys fine-grained preference strength. As a result, these models struggle to exploit graded semantics and nuanced preference distinctions. We propose Ordinal Semantic Anchoring (OSA), a hybrid CF-LLM framework that explicitly incorporates preference strength by modeling interaction-level user feedback. OSA represents ordinal preference levels as numeric textual tokens and uses their token embeddings as semantic anchors to align user-item interaction representations in the LLM latent space. Through strength-aware alignment across ordinal levels, OSA preserves preference semantics when integrating collaborative signals with LLMs. Experiments on multiple real-world datasets demonstrate that OSA consistently outperforms existing baselines, particularly in pairwise preference evaluation, highlighting its effectiveness in modeling fine-grained user preferences over prior CF-LLM methods.

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