14 citations · 28 across the 12 of their papers we have counts for
11 papers · 1 filter
Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval
Junyoung Kim, Anton Korikov, Jiazhou Liang +5
While Large Language Models (LLMs) exhibit exceptional zero-shot relevance modeling, their high computational cost necessitates framing passage retrieval as a budget-constrained gl…
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…
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…
Recommendation with Generative Models
Yashar Deldjoo, Zhankui He, Julian McAuley +8
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models ha…
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…
Multi-Aspect Reviewed-Item Retrieval via LLM Query Decomposition and Aspect Fusion
Anton Korikov, George Saad, Ethan Baron +3
While user-generated product reviews often contain large quantities of information, their utility in addressing natural language product queries has been limited, with a key challe…
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley +7
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample…
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Armin Toroghi +6
As natural language interfaces enable users to express increasingly complex natural language queries, there is a parallel explosion of user review content that can allow users to b…