Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation

Hongyang Su, Beibei Kong, Lei Cheng, Chengxiang Zhuo, Zang Li, Chenyun Yu

Keywords: Conversational Recommendation, Semantic ID Generation, News Recommendation, Cold-Start Reasoning, Hallucination Elimination

TL;DR: We replace retrieve-first pipelines with intent-driven Semantic ID generation and Generate-then-Match, achieving 0% hallucination and strong cold-start performance in conversational news recommendation.

Abstract: Conversational news recommendation requires grounding each suggestion in a rapidly evolving article corpus while addressing implicit user intents that lack explicit retrievable keywords. To characterize this scenario, we identify 6 intent types from production dialogues: five are implicit and pose fundamental challenges to standard RAG pipelines, forming a critical retrieve-first bottleneck. To address these issues, we introduce intent-driven Semantic ID (SID) generation under a Generate-then-Match paradigm. With two-stage training that consists of multi-task SID alignment and GPT-4 Chain-of-Thought distillation, an LLM maps diverse intents to hierarchical SID prefixes, which are then fuzzy-matched to the current news pool to guarantee fully grounded recommendations. Profile-Aware Dual-Signal Reasoning (PADR) further enables cold-start users to obtain valid recommendations using only profiles. On a mainstream Chinese news platform, our 7B model achieves 0% hallucination and 12.4% L1 match in the 152K open-generation SID space ($4 \times$ random baseline). It matches GPT-4+Hybrid RAG on L1 while surpassing it on finer-grained metrics (L2 $2 \times$, Category +1.2pp) at $\sim 100 \times$ lower cost. Cold-start users, where existing baselines score 0%, achieve 18.0% L1 ($6 \times$ random), the highest among all user groups.

Submission Type: Emerging

Submission Number: 446