14 citations · 28 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking
Sara Kemper, Justin Cui, Kai Dicarlantonio +4
Conversational recommendation (ConvRec) systems must understand rich and diverse natural language (NL) expressions of user preferences and intents, often communicated in an indirec…
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
David Eric Austin, Anton Korikov, Armin Toroghi +1
Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top item preferences in a cold-start setting is a key challenge for building effective and p…
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…