Branching Preferences: Visualizing Non-linear Topic Progression in Conversational Recommender Systems
Lovis Bero Suchmann, Nicole C. Krämer, Jürgen Ziegler · 2023
Recent advances in AI allow complex, natural user–system dialogue flow in NLP-based conversational recommender systems (CRS). While this enables users to express complex intents to the system, its usual linear GUI representation as a chat log fails to account for two non-linear aspects of natural conversation: humans can switch between topics as customary; and, especially in decision-making contexts, topics discussed are structurally related. As early work, we motivate and present a GUI design approach that aims to exploit these phenomena for CRS by conveying topic progression, and discuss several design variants, their trade-offs, and open questions. Our approach aims to help users orientate while exploring and comparing multiple preference model variants and corresponding recommendations in complex, natural ways, also accounting for different explanation types. Such orientation could benefit users for achieving complex goals using CRS, like thoroughly-informed decision making, getting inspiration for novel consumable items, and exploring their own preferences.