31 citations · 159 across the 14 of their papers we have counts for
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cs.IR2023★ 8 cited
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang +3
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).…
cs.IR2023★ 4 cited
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
Megan Leszczynski, Shu Zhang, Ravi Ganti +4
Recommender systems are ubiquitous yet often difficult for users to control, and adjust if recommendation quality is poor. This has motivated conversational recommender systems (CR…