Towards Understanding and Mitigating Unintended Biases in Language Model-driven Conversational Recommendation (LMRec)

We study a recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias --- i.e., due to language variations such as name references or indirect indicators of sexual orientation or location that should not affect recommendations --- manifests in significantly shifted price and category distributions of restaurant recommendations...
May 2022

Distributional Contrastive Embedding for Clarification-based Conversational Critiquing

Managing uncertainty in preferences is core to creating the next generation of conversational recommender systems (CRS). However, an often-overlooked element of conversational interaction is the role of clarification. Users are notoriously noisy at revealing their preferences, and a common error is being unnecessarily specific, e.g., suggesting "chicken fingers" when a restaurant with a "kids menu" was the intended preference...
May 2022