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