A TRUST BEHAVIOR BASED RECOMMENDER SYSTEM FOR SOFTWARE USAGE (INVITED) Zheng Yan XiDian University, China/Aalto University...
OUTLINE <ul><li>Introduction </li></ul><ul><li>Related work </li></ul><ul><li>Trust behavior construct </li></ul><ul><li>R...
INTRODUCTION <ul><li>Trust and usage </li></ul><ul><ul><li>Overcome perceptions of uncertainty and risk </li></ul></ul><ul...
MEASURE TRUST VIA TRUST BEHAVIORS <ul><li>Marsh: model trust behavior rather than trust  </li></ul><ul><li>Advantages of m...
OUR PAPER WORK <ul><ul><li>A trust behavior based recommender system for software usage. </li></ul></ul><ul><ul><ul><li>Ex...
RELATED WORK AND CURRENT CHALLENGES <ul><li>Recommender systems  </li></ul><ul><ul><li>Apply information filtering techniq...
TRUST BEHAVIOR CONSTRUCT <ul><li>Using behavior </li></ul><ul><ul><li>Normal application usage </li></ul></ul><ul><ul><ul>...
A COMPUTATIONAL TRUST MODEL <ul><li>: original trust value  </li></ul><ul><ul><li>personal motivation </li></ul></ul><ul><...
TRUST BEHAVIOR METRIC <ul><li>User trust behavior metric </li></ul>(2) (3)
RECOMMENDATION VECTOR (4) (5)
EXAMPLE BASED EVALUATION: ACCURACY <ul><li>10 users use three applications, with simulated  </li></ul><ul><li>For the 11 t...
CONCLUSIONS <ul><li>A recommender system for software usage (especially mobile applications) based on trust behavior corre...
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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

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

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