Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software_Na Li
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Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software_Na Li

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Presented at ITHET 2010

Presented at ITHET 2010

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Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software_Na Li Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software_Na Li Presentation Transcript

  • ITHET 29th April – 1st May 2010, Cappadocia, TurkeyTrust-Based Rating Prediction for Recommendationin Web 2.0 Collaborative Learning Social Software Na Li, Sandy El Helou, Denis Gillet Real-Time Coordination and Distributed Interaction Systems (ReAct) Automatic Control Lab, Swiss Federal Institute of Technology in Lausanne Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Outline•  Introduction•  Collaborative Learning Domain•  3A Interaction Model•  Trust-Based Rating Prediction Approach•  Evaluation and Results•  Conclusion and Future Work Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Introduction•  Web 2.0 social software ▫  A large amount of user generated content ▫  New challenge: selection of useful resources RSS Feeds Pictures PicturesWiki Pages Documents Videos Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Introduction•  Rating systems ▫  Evaluate quality of content in open environment ▫  Provide recommendation for different users Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Introduction•  Rating systems – application level Epinions   1 to 5 stars   A set of aspects for shops and products (ordering, delivery, service)   Status for members (Advisor, Top reviewer, Category Lead) ePractice.eu   Use “Kudos” to measure the activity of members   Award a number of “Kudos” according to each user action Everything2   “Positive” and “Negative” votes for articles   Users’ ranking according to their contribution•  Rating systems – academic research level ▫  TidalTrust (J. Golbeck), MoleTrust(P. Massa) ▫  User explicitly specifies a trust value towards another user ▫  Build trust network, Random walk in trust network ▫  Personalized rating prediction Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Collaborative Learning Domain•  Collaborative learning environment ▫  Unlike e-commerce and review sites ▫  Gift economy•  Rating systems ▫  Evaluate user generated content ▫  Filter helpful learning resources, peers and group activities ▫  Personalized rating prediction for recommendation Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • 3A Interaction Model Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Trust-Based Rating Prediction Approach•  Objective ▫  Build users’ trust network using 3A graph structure ▫  Personalize the rating prediction ▫  Infer trust value in an implicit way•  Basic idea ▫  What influences rating opinion: similarity and familiarity ▫  People tend to trust the opinions of acquaintance and those having similar interests and tastes. Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Trust-Based Rating Prediction Approach•  Trust measurement ▫  Multi-relational trust metric ▫  Build a “Web of Trust” for a particular user using heterogeneous types of relationships•  Trust Inference ▫  Direct trust ▫  Indirect trust Trust How Much? Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Trust-Based Rating Prediction Approach•  Direct trust (DT): derived from a particular type of relationship Is Member of Advanced Alice Algorithms Group ActivityW (Membership): weight of “membership” relationshipN (Alice, Membership): number of group activities Alice joins Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Trust-Based Rating Prediction Approach•  Trust propagation Bob•  Propagation distance (PD) ente d by m Com Rated by e Article Sara Creat Is Member French Has Member Alice Learning Luis Activity Rated by Video Ben Jack Propagate Propagate Propagate PD Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Trust-Based Rating Prediction Approach•  Indirect Trust (IT) Inference Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Trust-Based Rating Prediction Approach•  Rating prediction from a user to an item ▫  Using user’s “Web of Trust” ▫  People in “Web of Trust” are seen as trustable ▫  Average of all the rating scores given by trustable people, weighted by their trust value Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Evaluation and Results•  Using Remashed data set ▫  50 users, 6000 items, 3000 tags and 450 ratings ▫  “Leave-one-out” method ▫  Compare “predicted score – actual score” deviation of trust-based prediction and simple average Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Evaluation and Results•  Change parameters ▫  Weights for relationships doesn’t make a significant difference in rating prediction ▫  Increasing size of trust network might add noise, lead to bigger prediction error Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Conclusion and Future Work•  Propose a trust-based rating prediction approach, inferring trust in an implicit way•  Provide personalized rating prediction so as to evaluate user-generated content in collaborative learning environment•  Future deploy and evaluation will be conducted in a collaborative learning platform, namely Graaasp (graaasp.epfl.ch) Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
  • Questions? Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland