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

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

"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, Italy
September 15–18, 2009

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  • 25 minutes = 20 minutes pres. + 5 minutes questions.
  • CF algorithm generally make recommendations based on similarity between users. As mentioned, similarity measure is not sufficient when user profiles are sparse. A connection between how similar two users are and how much they trust each other, is discussed trust can be considered as a measure for expressing the relationship between two users in recommendation systems They depict that trust can be aggregated for all of the users in a social network, and the “importance”of a certain user is predicated by using a graph walking algorithm
  • Coverage measures the percentage of items that a recommender system can provide predictions for
  • Mean Absolute Error (MAE) measures the average absolute difference between a predicted rating made for a specific user and the user’s actual rating
  • Distribution of Indegree for the first ten of most trustworthy users with and without T-index. Indegree depicts incoming edges to a node as a user who is trusted by others. It is observed that by employing T-index, more centric users can be found which results in more clusters . Therefore, node’s weights in terms of incoming trust relationships are more balanced in trust graph of users, by utilizing Tindex.
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    • 1. 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
    • 2.
      • Motivation
      • Contribution
        • T-Index + TopTrustee
      • Approach
        • Framework
        • Ontologies
        • Trust Calculation
        • Metric Choice
        • Trust Transposure
        • Trust Propagation
        • Recommendation Prediction
      • Experiment
        • Coverage, 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) = 7
      • Indegree (Ub) = 5
      • T-index (Ua) = 2
      • T-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:
        • Transpose their values to have same rating scale
        • 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 length
        • Trust 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. 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
    • 18.
      • 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
    • 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 distribution most trustworthy users
    • 23. Trust network visualization Configuration: n=3 m=3 T-index=0
    • 24. Trust network visualization Configuration: n=3 m=3 T-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 Fazeli
        • http://www.isk.kth.se/~soude/
      • Nima Dokoohaki
        • http://web.it.kth.se/~nimad/
      • Misha Matskin
        • http://www.idi.ntnu.no/~misha/
      Questions
    • 27.
      • 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/
      Presenter

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