data mining and machine learning lab etrust: understanding trust evolution in an online world...
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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
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
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
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
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
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
Experiments
• Datasets
• Findings from the study of trust evolution
• Can eTrust help improve trust related applications?
- Rating Prediction
- Trust Prediction
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%)
Future Work
• Seek more applications for eTrust - Ranking evolution
- Recommendation systems
- Helpfulness prediction
• Generalize eTrust to other online worlds
- e-commerce