Paper Session 8: Conversations

Date: Wednesday, Aug 30, 2017, 09:30-10:15
Location: Room 1
Chair: Nava Tintarev

  • LPUnderstanding How People Use Natural Language to Ask for Recommendations by Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren Terveen, F. Maxwell Harper

    The technical barriers for conversing with recommender systems using natural language are vanishing. Already, there are commercial systems that facilitate interactions with an AI agent. For instance, it is possible to say “what should I watch” to an Apple TV remote to get recommendations. In this research, we investigate how users initially interact with a new natural language recommender to deepen our understanding of the range of inputs that these technologies can expect. We deploy a natural language interface to a recommender system, we observe users’ first interactions and follow-up queries, and we measure the differences between speaking- and typing-based interfaces. We employ qualitative methods to derive a categorization of users’ first queries (objective, subjective, and navigation) and follow-up queries (refine, reformulate, start over). We employ quantitative methods to determine the differences between speech and text, finding that speech inputs are typically longer and more conversational.

  • SPDefining and Supporting Narrative-driven Recommendation by Toine Bogers and Marijn Koolen

    Research into recommendation algorithms has made great strides in recent years. However, these algorithms are typically applied in relatively straightforward scenarios: given information about a user’s past preferences, what will they like in the future? Recommendation is often more complex, however: evaluating recommended items never takes place in a vacuum, and it is often a single step in the user’s more complex background task. In this paper, we define a specific type of recommendation scenario called narrative-driven recommendation, where the recommendation process is driven by both a log of the user’s past transactions as well as a narrative description of their current interest(s). Through an analysis of a set of real-world recommendation narratives from the LibraryThing forums, we demonstrate the uniqueness and richness of this scenario and highlight common patterns and properties of such narratives.

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