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Invited paper: A Trust Behavior based Recommender System for Software Usage by Zheng Yan
 

Invited paper: A Trust Behavior based Recommender System for Software Usage by Zheng Yan

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    Invited paper: A Trust Behavior based Recommender System for Software Usage by Zheng Yan Invited paper: A Trust Behavior based Recommender System for Software Usage by Zheng Yan Presentation Transcript

    • A TRUST BEHAVIOR BASED RECOMMENDER SYSTEM FOR SOFTWARE USAGE (INVITED) Zheng Yan XiDian University, China/Aalto University, Finland
    • OUTLINE
      • Introduction
      • Related work
      • Trust behavior construct
      • Recommendation generation
      • Experimental study
      • Conclusions and future work
    • INTRODUCTION
      • Trust and usage
        • Overcome perceptions of uncertainty and risk
        • Engages in trust behaviors
      • Trust behavior
        • A user’s actions to depend on software or believe the software could perform as expectation
      • Trust in software
        • Highly subjective, an internal ‘state’ of the user
        • Hard to be measured directly
    • MEASURE TRUST VIA TRUST BEHAVIORS
      • Marsh: model trust behavior rather than trust
      • Advantages of modeling trust behavior
        • Measure a subjective concept by evaluating it through objective trust behavior observation
        • Credible information is gained after using software and by observing the consequences of its performance
      • Current literature:
        • Few existing trust models explore trust in the view of human trust behaviors
        • Little work provides recommendations based on trust behaviors
    • OUR PAPER WORK
        • A trust behavior based recommender system for software usage.
          • Explore a model of trust behavior construct
          • Formalize this model to evaluate individual user’s trust in software through trust behavior observation
          • Design an algorithm to provide software recommendations based on the correlation of trust behaviors
        • Contributions
        • Achieve auto-data collection
        • Sound usability and enhance user privacy
        • Flexibly applied to recommend or select various software, especially for mobile applications
    • RELATED WORK AND CURRENT CHALLENGES
      • Recommender systems
        • Apply information filtering technique
        • Compare a user profile to some reference characteristics
        • Predict the 'rating’
        • Use trust as both weighting and filtering in recommendations.
      • Most characteristics are not based on the trust behavior -> an important clue of users’ preferences
      • Challenges
        • Privacy concern
        • Lacking uniform criteria
        • Subjective
    • TRUST BEHAVIOR CONSTRUCT
      • Using behavior
        • Normal application usage
          • elapsed usage time, number of usages and usage frequency.
      • Reflection behavior
        • Confronting software problems/errors or has good/bad usage experiences.
      • Correlation behavior
        • Correlated to a number of similarly functioned software
      • External factors
      Z. Yan, Y. Dong, V. Niemi, G.L. Yu. Exploring Trust of Mobile Applications Based on User Behaviors: An Empirical Study, Journal of Applied Social Psychology, 2011. (in press)
    • A COMPUTATIONAL TRUST MODEL
      • : original trust value
        • personal motivation
        • personality
        • software brand’s impact
        • perceived quality of the software’s execution platform
        • previous trust value in trust evaluation iteration.
      • Recommendation generation based on the correlation of three root constructs of trust behaviors
      (1) Z. Yan, R. Yan, “Formalizing Trust Based on Usage Behaviours for Mobile Applications”, ATC09, LNCS 5586, pp. 194-208, Brisbane, Australia, July, 2009.
    • TRUST BEHAVIOR METRIC
      • User trust behavior metric
      (2) (3)
    • RECOMMENDATION VECTOR (4) (5)
    • EXAMPLE BASED EVALUATION: ACCURACY
      • 10 users use three applications, with simulated
      • For the 11 th user who only consumes two applications a 0 and a 1 of the three, we calculate the recommendation vector w.r.t. a 2
      • Results
        • Z. Yan, P. Zhang, R.H. Deng, “TruBeRepec: A Trust-Behavior-Based Reputation and Recommender System for Mobile Applications”, Journal of Personal and Ubiquitous Computing, Springer, 2011. doi: 10.1007/s00779-011-0420-2
      • Observation
        • Personalized recommendations based on trust behavior correlation
          • A concrete clue of interest similarity and preferences
    • CONCLUSIONS
      • A recommender system for software usage (especially mobile applications) based on trust behavior correlation
      • Overcome three challenges
        • hard to collect user preferences due to privacy concerns;
        • lack uniform criteria for recommendations;
        • diverse opinions on recommendations without personalization.
      • Future work: easy acceptance
        • Implementation
        • Performance evaluation based on real user data