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"Social Trust-aware Recommendation System: A T-Index Approach"Workshop on Web Personalization, Reputation and Recommender Systems (WPRRS09)Held in conjunction with 2009 IEEE/ WIC/ ACM International Conference on Web Intelligence (WI 2009) and Intelligent Agent Technology,http://www.wprrs.scitech.qut.edu.au/Università degli Studi di Milano Bicocca, Milano, ItalySeptember 15–18, 2009
Citation preview
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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
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• Motivation• Contribution
T-Index + TopTrustee
• ApproachFrameworkOntologiesTrust Calculation
Metric ChoiceTrust TransposureTrust PropagationRecommendation Prediction
• ExperimentCoverage, MAE, Indegree
• Conclusion
Agenda
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• 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
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• 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
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• 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
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TopTrustee Idea Depicted
An example of TopTrustee
Finding trustworthy users across the trust network even outside the traversal path length limit
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T-index Idea Depicted
An example of T-index
Indegree (Ua) = 7Indegree (Ub) = 5
T-index (Ua) = 2T-index (Ub) = 4
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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
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User Ontology
•Relationship:•Top-n Trustees
•Rank Relation:•History of rating
•T-index:•User's T-index
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Item Ontology
• Ontological Item Profile
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• 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
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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
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• 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
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• 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
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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
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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
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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
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• 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
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• 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
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Coverage
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MAE
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Indegree distributionmost trustworthy users
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Trust network visualizationConfiguration:n=3m=3T-index=0
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Trust network visualizationConfiguration:n=3m=3T-index=100
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• 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
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• 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