Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

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 …

Online recommendation systems are prone to create filter bubbles, whereby users are only
recommended content narrowly aligned with their historical interests. In the case of media …

Keyphrase generation, which can help people obtain key information from a long document
(social media posts or scientific articles) in a short time, has made significant progress in …

Keyphrase extraction is a critical task in text information retrieval, which traditionally employs
both supervised and unsupervised approaches. Supervised methods generally rely on large …

Computational preference elicitation methods are tools used to learn people's preferences
quantitatively in a given context. Recent works on preference elicitation advocate for active …

Despite the impressive performance of large language models (LLMs) across various
benchmarks, their ability to address ambiguously specified problems--frequent in real-world …

Managing uncertainty in preferences is core to creating the next generation of
conversational recommender systems (CRS). However, an often-overlooked element of …

Abstract Language-based Conversational Recommender Systems (CRSs) have attracted
growing attention as they allow users to express and interactively refine their preferences in …

Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …