Conversational recommenders with adaptive suggestions

ABSTRACT

We consider a conversational recommender system based on example-critiquing where some recommendations are suggestions aimed at stimulating preference expression to acquire an accurate preference model. User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32].

This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples.

We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Human factors

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Software psychology

General Terms:
Human Factors

Keywords:
example-critiquing interfaces, personalized search, recommender systems