invited paper: a trust behavior based recommender system for software usage by zheng yan
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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
Personal motivation
Brand impact
Perceived quality
Personality
Using behavior
Reflection behavior
Correlation behavior
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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
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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
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RECOMMENDATION VECTOR
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EXAMPLE BASED EVALUATION: ACCURACY
10 users use three applications, with simulated
For the 11th user who only consumes two applications a0 and a1 of the three, we calculate the recommendation vector w.r.t. a2
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
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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