用多模态融合与受限LLM重排，提升对话式音乐推荐效果。
Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation
- 融合7种嵌入空间与关键词检索，加权融合提升召回精度。
- 优化重排策略，使推荐结果更符合用户历史与多样性需求。
- 发现受限LLM注入可显著提分，适合追求高精度的推荐系统研究者。
我们提出Team Semiintelligencn在ACM RecSys 2026 TalkPlayData挑战中的解决方案，针对对话式音乐推荐构建了一个多模态个性化推荐系统。系统采用三阶段流程：(1) 多模态检索，整合七种密集嵌入空间——曲目与用户级CF-BPR、Qwen3（元数据、歌词、属性）、CLAP音频、SigLIP视觉——并结合BM25词法检索和艺术家子串匹配信号，通过加权倒数排名融合（RRF）融合，优化信号权重；(2) 轻量级重排（历史过滤、流行度平滑、馆藏多样性惩罚）；(3) 使用GPT-4o-mini生成人格化回复。此外，开发阶段测试了受限LLM引导的艺术家注入、专辑延续信号、XGBoost LambdaMART及更优的GPT-4.1提示，但因成本与复杂性未部署至Blind B。通过差分进化在500会话开发集上优化RRF权重，使MRR提升+19.5%。在Blind A上，无约束的LLM注入导致nDCG大幅下降（-18.9%），而仅对9个会话进行保守注入表现最佳，此结果为盲评观察，需进一步验证。最终系统在Blind B上取得复合得分0.3213。
原文摘要 · Abstract (English)
We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a multi-modal and personalized conversational recommender system. Our submitted system employs a three-stage pipeline: (1) multi-modal retrieval constructing decay-weighted centroids across seven dense embedding spaces - track- and user-level CF-BPR, Qwen3 (metadata, lyrics, attributes), CLAP audio, and SigLIP visual - supplemented by BM25 lexical retrieval and an artist substring-match signal, all fused via weighted Reciprocal Rank Fusion (RRF) with optimized signal weights; (2) lightweight reranking (history filtering, popularity smoothing, and catalog diversity penalization); and (3) persona-diversified response generation using GPT-4o-mini. Beyond this submitted configuration, we report development-time experiments with additional components - constrained LLM-guided artist injection, album continuation signals, XGBoost LambdaMART, and a superior GPT-4.1 response prompt - that were not deployed to Blind B due to cost and complexity constraints. We optimize RRF weights on a 500-session development split via differential evolution, improving MRR by +19.5%. On Blind A, we observe that unconstrained LLM-guided injection across 54 sessions causes catastrophic nDCG regression (-18.9%), while conservative injection on only 9 sessions yields the best observed Blind A nDCG - a finding we present as a Blind A observation warranting further validation. The submitted system achieves a Blind B composite score of 0.3213.
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