eTrust: Understanding Trust Evolution in an Online World - PowerPoint Presentation

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eTrust: Understanding Trust Evolution in an Online World

Jiliang Tang, Huiji Gao and Huan Liu Computer Science and Engineering Arizona State UniversityAtish Das SarmaeBay Research Lab eBay Inc.

August 12-16, 2012 KDD2012

Slide2

Trust and Its Evolution

Trust plays an important role in helping online users collect reliable information

Abundant research on static trust for making good decisions and finding high quality content

However, trust evolves as people interact and time passes by

It is necessary to study its evolution

Its study can advance online trust research for trust related applications

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Our Contributions

We identify the differences of trust study in physical and online worlds

We investigate how to study online trust evolution

We show if this study can help improve the performance of trust related applications

Slide4

Research in Physical and Online Worlds

Trust evolution in a physical world

- Step 1: inviting a group of participants ( a small group)

- Step 2: recording their sociometric information

- Step 3: recording conditions or situations for the change

Differences encountered in an online world

- Users are world-widely distributed

- Sociometric information on trust is unavailable

- Passive observation is the modus operandi to gather data

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Studying Online Trust Evolution

Overcoming the challenge of passive observation

Where can we find relevant data for trust study (an issue about environment)

How can we infer about the information about trust (an issue about methodology)

Modeling online trust evolution

How to incorporate social theories mathematically

Evaluating the gain of trust evolution study

Rating prediction and trust prediction

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Online Rating System

time t

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Online Rating System

time t

time t+1

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Online Rating System

time t

time t+1

Temporal Information

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Social Science theories

Correlations between rating and user preference

- Dynamics of rating

Correlations between user preference and trust

- Drifting user preferences

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Methodology for Trust Evolution

Trust Evolution

Dynamics of user preference

Temporal information, rating etc

Online Rating System

Social theories

Social theories

Rating Prediction

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Our Framework: eTrust

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Components of eTrust

Part 4

Part 3

Part 2

Part 1

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Part 1: Modeling Rating via User Preference

Rating is related to user preference and item characteristic - - is the preference of i-th user in time t, is the characteristic of j-th item and K is the number of latent facets of items

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Part 2: Modeling Rating via Trust Network

People is likely to be influenced by their trust networks

Trust strength between

i

-th and v-th users in the k-th facet

Decaying the earlier rating

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Part 3: Modeling Trust and User preference

Modeling the correlation between trust and user preference is preference similarity vector in the k-th facet and is a user specific bias

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Part 4: Modeling Change of User Preference

Modeling the change of user preference c is a function to control how user preference change, λ controls the speed of change

Slide17

Experiments

Datasets

Findings from the study of trust evolution

Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Slide18

Experiments

Datasets

Findings from the study of trust evolution

Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

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Datasets

EpinionsProduct review sitesStatistics

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http://www.public.asu.edu/~jtang20/datasetcode/truststudy.htm

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Splitting the Dataset

Epinions is separated into 11 timestamps

11

thJan, 2001,

11

th

Jan, 2010,

…….

11

th

Jan, 2009,

11thJan, 2002,

T2

T1

T10

T

11

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Experiments

Datasets

Findings from the study of trust evolution

Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

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Speed of Change of Trust

The evolution speed of an open triad is 6.12 times of that of a closed triad

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User preferences drift over time

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The speed of change varies with people and facets

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Experiments

Datasets

Findings from the study of trust evolution

Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Slide27

Experiments

Datasets

Findings from the study of trust evolution

Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

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Applications of eTrust: Rating Prediction

Given ratings before T, we predict ratings in T+1 as,

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Testing Datasets

W

e

further divide

data in T

11

into

two testing datasets

-

N

: the

ratings involved in new items or new

users(

10.06

%)

-

K

: the

remaining

ratings

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Comparison of Rating Prediction

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Experiments

Datasets

Findings from the study of trust evolution

Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

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Applications of eTrust: Trust Prediction

The likelihood of trust establishing is estimated as,

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Testing Datasets

W

e also divide data in T

11

into

two testing datasets

-

E

: trust

relations established among

existing

users

-

N

:

trust relations involved in new

users (23.51%)

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Comparison of Trust Prediction

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Future Work

Seek more applications for eTrust

- Ranking evolution

- Recommendation systems

- Helpfulness prediction

Generalize eTrust to other online worlds

- e-commerce

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Questions

Acknowledgments:

This work is, in part, sponsored by ARO via a grant (#025071). Comments and suggestions from DMML members and reviewers are greatly appreciated.