5 papers
Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments
Yifan Liu, Qianfeng Wen, Jiazhou Liang +6
Natural Language Recommendation (NLRec) generates item suggestions based on the relevance between user-issued NL requests and NL item description passages. Existing NLRec approache…
A Simple but Effective Elaborative Query Reformulation Approach for Natural Language Recommendation
Qianfeng Wen, Yifan Liu, Justin Cui +4
Natural Language (NL) recommender systems aim to retrieve relevant items from free-form user queries and item descriptions. Existing systems often rely on dense retrieval (DR), whi…
Batched Self-Consistency Improves LLM Relevance Assessment and Ranking
Anton Korikov, Pan Du, Scott Sanner +1
LLM query-passage relevance assessment is typically studied using a one-by-one pointwise (PW) strategy where each LLM call judges one passage at a time. However, this strategy requ…
Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation
Qianfeng Wen, Yifan Liu, Joshua Zhang +4
In Query-driven Travel Recommender Systems (RSs), it is crucial to understand the user intent behind challenging natural language(NL) destination queries such as the broadly worded…
Multi-modal Generative Models in Recommendation System
Arnau Ramisa, Rene Vidal, Yashar Deldjoo +8
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With t…