← 研踪 PaperTrailarXiv:2611.11346 · v1
IntentLens: Grounding Underspecified Multimodal Queries for Recommendation via Tool-Augmented Reasoning
首次提交 2026-11-08 20:49
版本更新 2026-11-08 20:49
雷达观测 2026-11-10 12:16
摘要
As intelligent personal assistants are increasingly deployed in real-world scenarios, recommendation paradigms are shifting from passive preference matching to inferring user intent from underspecified multimodal inputs. In practical interactions, users typically issue a reference image paired with a vague natural language query, keeping key visual attributes, personalized preferences, and item-side constraints implicit. Existing recommendation methods either leverage historical user interactions augmented with multimodal item features or assume fully articulated textual queries; both paradigms struggle to resolve underspecified multimodal queries for accurate item matching. To address this limitation, we propose IntentLens, a tool-augmented recommendation framework that grounds ambiguous multimodal queries into query-relevant evidence prior to user-item matching. IntentLens follows a ground-then-rank pipeline: a shared multimodal LLM leverages visual tools, user memory modules, and item attribute retrieval tools to (i) infer latent user intent from image-text inputs and (ii) augment each candidate item with fine-grained, query-conditioned evidence. Subsequently, a lightweight ranker scores candidates based on the grounded representations. To facilitate research in this novel setting, we construct two benchmarks with underspecified multimodal queries, namely \textsc{GoogleReview-MMTool} and \textsc{Yelp-MMTool}. Extensive experiments on these benchmarks demonstrate that IntentLens outperforms competitive MLLM and retrieval baselines, and exhibits strong generalization to unseen categories and datasets.
作者备注：NeurIPS 2026
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-08 20:49 · 最新观测版本
IntentLens: Grounding Underspecified Multimodal Queries for Recommendation via Tool-Augmented Reasoning
As intelligent personal assistants are increasingly deployed in real-world scenarios, recommendation paradigms are shifting from passive preference matching to inferring user intent from underspecified multimodal inputs. In practical interactions, users typically issue a reference image paired with a vague natural language query, keeping key visual attributes, personalized preferences, and item-side constraints implicit. Existing recommendation methods either leverage historical user interactions augmented with multimodal item features or assume fully articulated textual queries; both paradigms struggle to resolve underspecified multimodal queries for accurate item matching. To address this limitation, we propose IntentLens, a tool-augmented recommendation framework that grounds ambiguous multimodal queries into query-relevant evidence prior to user-item matching. IntentLens follows a ground-then-rank pipeline: a shared multimodal LLM leverages visual tools, user memory modules, and item attribute retrieval tools to (i) infer latent user intent from image-text inputs and (ii) augment each candidate item with fine-grained, query-conditioned evidence. Subsequently, a lightweight ranker scores candidates based on the grounded representations. To facilitate research in this novel setting, we construct two benchmarks with underspecified multimodal queries, namely \textsc{GoogleReview-MMTool} and \textsc{Yelp-MMTool}. Extensive experiments on these benchmarks demonstrate that IntentLens outperforms competitive MLLM and retrieval baselines, and exhibits strong generalization to unseen categories and datasets.
查看此版本解读 →查看此版本原文 ↗