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UtilSim: Iteratively Helping Users Discover Their Preferences

  • Saurabh Gupta
  • Sutanu Chakraborti
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 152)

Abstract

Conversational Recommender Systems belong to a class of knowledge based systems which simulate a customer’s interaction with a shopkeeper with the help of repeated user feedback till the user settles on a product. One of the modes for getting user feedback is Preference Based Feedback, which is especially suited for novice users(having little domain knowledge), who find it easy to express preferences across products as a whole, rather than specific product features. Such kind of novice users might not be aware of the specific characteristics of the items that they may be interested in, hence, the shopkeeper/system should show them a set of products during each interaction, which can constructively stimulate their preferences, leading them to a desirable product in subsequent interactions. We propose a novel approach to conversational recommendation, UtilSim, where utilities corresponding to products get continually updated as a user iteratively interacts with the system, helping her discover her hidden preferences in the process. We show that UtilSim, which combines domain-specific “dominance” knowledge with SimRank based similarity, significantly outperforms the existing conversational approaches using Preference Based Feedback in terms of recommendation efficiency.

Keywords

Knowledge based Recommendation Preference Based Feedback Utility estimation Case Based Recommendation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Saurabh Gupta
    • 1
  • Sutanu Chakraborti
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

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