6 papers
Large Neighborhood Search meets Iterative Neural Constraint Heuristics
Yudong W. Xu, Wenhao Li, Scott Sanner +1
Neural networks are being increasingly used as heuristics for constraint satisfaction. These neural methods are often recurrent, learning to iteratively refine candidate assignment…
ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation
Anton Korikov, Scott Sanner
LLM-reranking is limited by the top-k documents retrieved by vector similarity, which neither enables contextual query-document token interactions nor captures multimodal relevance…
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…
Large Language Model Driven Recommendation
Anton Korikov, Scott Sanner, Yashar Deldjoo +8
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unloc…
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…