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Usability Guidelines for Product Recommenders Based on Example Critiquing Research

  • Pearl Pu
  • Boi Faltings
  • Li Chen
  • Jiyong Zhang
  • Paolo Viappiani
Chapter

Abstract

Over the past decade, our group has developed a suite of decision tools based on example critiquing to help users find their preferred products in e-commerce environments. In this chapter, we survey important usability research work relative to example critiquing and summarize the major results by deriving a set of usability guidelines. Our survey is focused on three key interaction activities between the user and the system: the initial preference elicitation process, the preference revision process, and the presentation of the systems recommendation results. To provide a basis for the derivation of the guidelines, we developed a multi-objective framework of three interacting criteria: accuracy, confidence, and effort (ACE). We use this framework to analyze our past work and provide a specific context for each guideline: when the system should maximize its ability to increase users’ decision accuracy, when to increase user confidence, and when to minimize the interaction effort for the users. Due to the general nature of this multi-criteria model, the set of guidelines that we propose can be used to ease the usability engineering process of other recommender systems, especially those used in e-commerce environments. The ACE framework presented here is also the first in the field to evaluate the performance of preference-based recommenders from a user-centric point of view.

Designers can use these guidelines for the implementation of an effective and successful product recommender.

Keywords

Recommender System Product Recommender Preference Elicitation Decision Accuracy Usability Guideline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Pearl Pu
    • 1
  • Boi Faltings
    • 2
  • Li Chen
    • 1
  • Jiyong Zhang
    • 1
  • Paolo Viappiani
    • 3
  1. 1.Human Computer Interaction Group, School of Computer and Communication SciencesSwiss Federal Institute of Technology in Lausanne (EPFL)LausanneSwitzerland
  2. 2.Artificial Intelligence Laboratory, School of Computer and Communication SciencesSwiss Federal Institute of Technology in Lausanne (EPFL)LausanneSwitzerland
  3. 3.Department of Computer ScienceUniversity of TorontoTorontoCanada

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