1

Social Trust-Aware Recommendation System:

A T-index ApproachAlireza Zarghami

Soude FazeliNima DokoohakiMihhail 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

2

• Motivation• Contribution

T-Index + TopTrustee

• ApproachFrameworkOntologiesTrust Calculation

Metric ChoiceTrust TransposureTrust PropagationRecommendation Prediction

• ExperimentCoverage, MAE, Indegree

• Conclusion

Agenda

3

• 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

4

• 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

5

• 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

6

TopTrustee Idea Depicted

An example of TopTrustee

Finding trustworthy users across the trust network even outside the traversal path length limit

7

T-index Idea Depicted

An example of T-index

Indegree (Ua) = 7Indegree (Ub) = 5

T-index (Ua) = 2T-index (Ub) = 4

8

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

9

User Ontology

•Relationship:•Top-n Trustees

•Rank Relation:•History of rating

•T-index:•User's T-index

10

Item Ontology

• Ontological Item Profile

11

• 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:

1. Transpose their values to have same rating scale

2. Calculate their mutual Trust

Trust Metric and Calculation

12

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

13

• 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

14

• 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

15

Collecting Recommendation for users

Recommendations for a particular user are collected by asking from its direct or indirect neighbors through traverals.

Limiting traversal lengthTrust threshold √Path length

For instance:Um is more trustworthy than Ug for Ua

16

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

17

RecommendationPrediction• 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

18

• MovieLenshttp://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

19

• 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

20

Coverage

21

MAE

22

Indegree distributionmost trustworthy users

23

Trust network visualizationConfiguration:n=3m=3T-index=0

24

Trust network visualizationConfiguration:n=3m=3T-index=100

25

• 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

26

• Thanks

• Contacts: -----------• Alireza Zarghami

http://www.isk.kth.se/~zarghami/

• Soude Fazelihttp://www.isk.kth.se/~soude/

• Nima Dokoohakihttp://web.it.kth.se/~nimad/

• Misha Matskinhttp://www.idi.ntnu.no/~misha/

Questions