Search Results for author:
Found 16 papers, 3 papers with code
``I'd rather just go to bed'': Understanding Indirect Answers
We revisit a pragmatic inference problem in dialog: Understanding indirect responses to questions.
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Inspired by recent successes of prompting paradigms for large language models (LLMs), we study their use for making recommendations from both item-based and language-based preferences in comparison to state-of-the-art item-based collaborative filtering (CF) methods.
Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems
Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users' choices.
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Users in consumption domains, like music, are often able to more efficiently provide preferences over a set of items (e. g. a playlist or radio) than over single items (e. g. songs).
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
This has motivated conversational recommender systems (CRSs), with control provided through natural language feedback.
Resolving Indirect Referring Expressions for Entity Selection
We address this problem of reference resolution, when people use natural expressions to choose between the entities.
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years.
Conversational Information Seeking
Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system.
Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions
These strategies do not perform well in cases where the user does not have sufficient knowledge of the target domain to answer such questions.
On Interpretation and Measurement of Soft Attributes for Recommendation
We address how to robustly interpret natural language refinements (or critiques) in recommender systems.
Untangle: Critiquing Disentangled Recommendations
Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy.
"I'd rather just go to bed": Understanding Indirect Answers
We revisit a pragmatic inference problem in dialog: understanding indirect responses to questions.
Common Conversational Community Prototype: Scholarly Conversational Assistant
This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions.
Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences
Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale.
Learning diverse rankings with multi-armed bandits
Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents.
