Data Mining and Machine Learning Lab

eTrust: Understanding Trust Evolution in an Online World

Jiliang Tang, Huiji Gao and Huan Liu Computer Science and Engineering Arizona State University

Atish Das SarmaeBay Research Lab

eBay Inc.

August 12-16, 2012 KDD2012

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

Our Contributions

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

2. We investigate how to study online trust evolution

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

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

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

Online Rating System

time t

Online Rating System

time t time t+1

Online Rating System

time t time t+1Temporal Information

Social Science theories

• Correlations between rating and user preference

- Dynamics of rating

• Correlations between user preference and trust

- Drifting user preferences

Methodology for Trust Evolution

Trust Evolution

Dynamics of user preference

Temporal information, rating etc

Online Rating System

Social theories Social theories

Rating Prediction

Our Framework: eTrust

Components of eTrust

Part 4Part 3

Part 2

Part 1

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

tip jq

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

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

tivks ib

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

Experiments

• Datasets

• Findings from the study of trust evolution

• Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Experiments

• Datasets

• Findings from the study of trust evolution

• Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Datasets

• Epinions- Product review sites

- Statistics

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

Splitting the Dataset

• Epinions is separated into 11 timestamps

11thJan, 2001,

11thJan, 2010,

…….

11thJan, 2009,

11thJan, 2002,

T2T1 T10 T11

Experiments

• Datasets

• Findings from the study of trust evolution

• Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Speed of Change of Trust

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

User preferences drift over time

The speed of change varies with people and facets

Experiments

• Datasets

• Findings from the study of trust evolution

• Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Experiments

• Datasets

• Findings from the study of trust evolution

• Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Applications of eTrust: Rating Prediction

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

Testing Datasets

• We further divide data in T11 into two testing datasets

- N: the ratings involved in new items or new users(10.06%)

- K: the remaining ratings

Comparison of Rating Prediction

Experiments

• Datasets

• Findings from the study of trust evolution

• Can eTrust help improve trust related applications?

- Rating Prediction

- Trust Prediction

Applications of eTrust: Trust Prediction

• The likelihood of trust establishing is estimated as,

Testing Datasets

• We also divide data in T11 into two testing datasets

- E: trust relations established among existing users

- N: trust relations involved in new users (23.51%)

Comparison of Trust Prediction

Future Work

• Seek more applications for eTrust - Ranking evolution

- Recommendation systems

- Helpfulness prediction

• Generalize eTrust to other online worlds

- e-commerce

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.