LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems

• URL: http://arxiv.org/abs/2507.04722v2
• Date: Sun, 20 Jul 2025 01:31:22 GMT
• Title: LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems
• Abstract summary: Only 10% of head movies account for nearly half of all mentions, whereas about 70% of tail movies receive merely 26% of the attention.<n>This imbalance gives rise to three critical challenges: head over-fitting, body representation drift, and tail sparsity.<n>We propose LumiCRS, an end-to-end framework that adjusts long-tail imbalance through three mutually reinforcing layers.
• Score: 2.4786559575079083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational recommender systems (CRSs) often suffer from an extreme long-tail distribution of dialogue data, causing a strong bias toward head-frequency blockbusters that sacrifices diversity and exacerbates the cold-start problem. An empirical analysis of DCRS and statistics on the REDIAL corpus show that only 10% of head movies account for nearly half of all mentions, whereas about 70% of tail movies receive merely 26% of the attention. This imbalance gives rise to three critical challenges: head over-fitting, body representation drift, and tail sparsity. To address these issues, we propose LumiCRS, an end-to-end framework that mitigates long-tail imbalance through three mutually reinforcing layers: (i) an Adaptive Comprehensive Focal Loss (ACFL) that dynamically adjusts class weights and focusing factors to curb head over-fitting and reduce popularity bias; (ii) Prototype Learning for Long-Tail Recommendation, which selects semantic, affective, and contextual prototypes to guide clustering and stabilize body and tail representations; and (iii) a GPT-4o-driven prototype-guided dialogue augmentation module that automatically generates diverse long-tail conversational snippets to alleviate tail sparsity and distribution shift. Together, these strategies enable LumiCRS to markedly improve recommendation accuracy, diversity, and fairness: on the REDIAL and INSPIRED benchmarks, LumiCRS boosts Recall@10 and Tail-Recall@10 by 7-15% over fifteen strong baselines, while human evaluations confirm superior fluency, informativeness, and long-tail relevance. These results demonstrate the effectiveness of multi-layer collaboration in building an efficient and fair long-tail conversational recommender.

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