Social Trust-aware Recommendation System: A T-Index Approach

Social Trust-Aware Recommendation System: A T-index Approach Alireza Zarghami Soude Fazeli Nima Dokoohaki Mihhail Matskin Presented at Workshop on Web Personalization, Reputation and Recommender Systems ( WPRRS’09 ) In conjunction with IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology ( WI’09 and IAT’09). September 2009

Motivation
Contribution
T-Index + TopTrustee
Approach
Framework
Ontologies
Trust Calculation
Metric Choice
Trust Transposure
Trust Propagation
Recommendation Prediction
Experiment
Coverage, MAE, Indegree
Conclusion
Agenda

Memory-based
Utilize the entire user-item data to predict likeness;
NNR, Pearson, statistical approach
Model-Based
Clustering, Bayesian, Rule based, Probabilistic Approach
Trust-Based √
Correlation between trust and similarity (proved by Golbeck, Massa/Avesani)
Collaborative Filtering heuristics

Model a recommendation system
Utilizes a distributed trust-based CF
Utilizes Semantic Web Ontology to deal with heterogeneous networks of users and items
Ability to traverse the trust networks to collect Recommendations
To have better coverage and prediction accuracy in short traversal by optimizing the trust network maintenance mechanism
Contribution

Our work is based upon two main ideas:
T-index
A measure inspired by Hindex to discover the agents within our trust network who provide trust values higher or equal to T.
TopTrustee
A list, which provides information about users who might not be accessible within a predefined maximum path length.
TopTrustee List=
(m) raters who provide Highest T-index values.
TopTrustee/ T-index

TopTrustee Idea Depicted
An example of TopTrustee
Finding trustworthy users across the trust network even outside the traversal path length limit

T-index Idea Depicted
An example of T-index
Indegree (Ua) = 7
Indegree (Ub) = 5
T-index (Ua) = 2
T-index (Ub) = 4

Ontological Model
Framework can deal with heterogeneous networks of user and item in a distributed manner
Users from different groups can be hosted by different servers possibly located in different organization for sake of privacy, accessible by their URI

User Ontology
Relationship:
Top-n Trustees
Rank Relation:
History of rating
T-index:
User's T-index

Item Ontology
Ontological Item Profile

Choice of trust metric
(common case) Trust value defined as a decimal value [0,1]
For users who find each other through TopTrustee list, calculated directly based on their common item in two steps:
Transpose their values to have same rating scale
Calculate their mutual Trust
Trust Metric and Calculation

Transposure of Trustee Rating
Different users have different scales for rating,
Row : Truster
Column : Trustee
tr(5)=4.43 Trustee' rating of 5 is considered as 4.43 for Truster to calculate trust

After transposing rating values of trustee to the same scale rating of truster, we compute their mutual trust value based on this formula*
Formula calculates the sum of their differences in rating values for common items divided by the number of truster's item multiplied by the maximum rating value.
* N. Lathia, S. Hailes and L. Capra. “Trust-based collaborative
filtering”, in IFIPTM 2008: Joint iTrust and PST Conferences on
Privacy, Trust Management and Security, P.14, London, 2008.
Trust Computation

Basic approach
For users who has no direct trust relationship, we propagate trust by multiplying trust values of the nodes are located in the path between them.
Trust Propagation

Collecting Recommendation for users
Recommendations for a particular user are collected by asking from its direct or indirect neighbors through traverals.
Limiting traversal length
Trust threshold √
Path length
For instance:
Um is more trustworthy than Ug for Ua

Recommendation – traversal path length
we just collect recommendations from short traversal length , so all traversals are limited to a predefined maximum traversal path length.
If the maximum defined as 3, traversal can not go further than Um regardless of trust value

Recommendation Prediction
Prediction of the Recommendations collected from direct or indirect neighbors are done by the weighted average of their rating based on their trust values calculated either through computation or propagation

MovieLens
http://www.movielens.org/
100,000 rating of 5-point scale
943 users and 1682 movies
Rating are sorted according to their timestamps
80% of rating used to build the network
20% of rating used to test the recommendations
Experiment - Dataset

Parameters
N number of neighbors per user (2,3,5,10,20,50)
M number of TopTrustee per item (2,3,5,7)
With or without T-index (0,100)
Trust threshold is defined as 0.1
Maximum path length of traversal is defined as 3
Experiments
MAE
Coverage
Indegree distribution of most trustworthy users
Experiment Types / Parameters

Coverage

Indegree distribution most trustworthy users

Trust network visualization Configuration: n=3 m=3 T-index=0

Trust network visualization Configuration: n=3 m=3 T-index=100

Designed an ontological model to model heterogeneous networks of users and items
Introduced TopTrustee list to enhance the process of discovering neighbors
Introduced T-index as a measure of trustworthiness
which can improve the Coverage and MAE in short traversal path
length, especially for small size of neighbors
T-index can improve trust network structure by increasing the number of well connected clusters
Conclusion

Thanks 
Contacts:
-----------
Alireza Zarghami
http://www.isk.kth.se/~zarghami/
Soude Fazeli
http://www.isk.kth.se/~soude/
Nima Dokoohaki
http://web.it.kth.se/~nimad/
Misha Matskin
http://www.idi.ntnu.no/~misha/
Questions

Nima Dokoohaki
Software and Computer Systems ( SCS ), Department of Electronics ,Computer and
Software Systems ( ECS ), School of Information and Communications Technology ( ICT ), Royal Institute of Technology ( KTH ), Stockholm, Sweden Office: +46 (0) 8 790 4149 Cell : +46 (0) 76 269 76 30 Fax: +46 (0) 8 751 1793 Email : nimad@kth.se
http://web.it.kth.se/~nimad/
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Social Trust-aware Recommendation System: A T-Index Approach

by Nima Dokoohaki on Sep 20, 2009

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