Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software<br />ITHET  29th April ...
Outline<br />Introduction<br />Collaborative Learning Domain<br />3A Interaction Model<br />Trust-Based Rating Prediction ...
Introduction<br />Web 2.0 social software<br />A large amount of user generated content<br />New challenge: selection of u...
Introduction<br />Rating systems<br />Evaluate quality of content in open environment<br />Provide recommendation for diff...
Introduction<br />Rating systems – application level<br />Rating systems – academic research level<br />TidalTrust (J. Gol...
Collaborative Learning Domain<br />Collaborative learning environment<br />Unlike e-commerce and review sites<br />Gift ec...
3A Interaction Model<br />
Trust-Based Rating Prediction Approach<br />Objective<br />Build users’ trust network using 3A graph structure<br />Person...
Trust-Based Rating Prediction Approach<br />Trust measurement<br />Multi-relational trust metric<br />Build a “Web of Trus...
Trust-Based Rating Prediction Approach<br />Direct trust (DT): derived from a particular type of relationship<br />W (Memb...
Trust-Based Rating Prediction Approach<br />Trust propagation<br />Propagation distance (PD)<br />Bob<br />Commented by<br...
Trust-Based Rating Prediction Approach<br />Indirect Trust (IT) Inference<br />
Trust-Based Rating Prediction Approach<br />Rating prediction from a user to an item<br />Using user’s “Web of Trust”<br /...
Evaluation and Results<br />Using Remashed data set<br />50 users, 6000 items, 3000 tags and 450 ratings<br />“Leave-one-o...
Evaluation and Results<br />Change parameters<br />Weights for relationships doesn’t make a significant difference in rati...
Conclusion and Future Work<br />Propose a trust-based rating prediction approach, inferring trust in an implicit way<br />...
Questions?<br />
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  • Trust propagates layer by layer, until reaching the propagate distance we predefine.A “Web of Trust” is constructed in this way.
  • Different weights and propagate distances are tried.On this test set, the change of trust weights for relationships doesn’t make a significant difference in the results of rating prediction.We get an optimal propagate distance value, which indicates that, instead of improving the prediction results, increasing the size of trust network might add noise, leading to bigger prediction error.
  • Different weights and propagate distances are tried.On this test set, the change of trust weights for relationships doesn’t make a significant difference in the results of rating prediction.We get an optimal propagate distance value, which indicates that, instead of improving the prediction results, increasing the size of trust network might add noise, leading to bigger prediction error.
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    1. 1. Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software<br />ITHET 29th April – 1st May 2010, Cappadocia, Turkey<br />Na Li, Sandy El Helou, Denis Gillet<br />Real-Time Coordination and Distributed Interaction Systems (ReAct) <br />Automatic Control Lab, Swiss Federal Institute of Technology in Lausanne<br />
    2. 2. Outline<br />Introduction<br />Collaborative Learning Domain<br />3A Interaction Model<br />Trust-Based Rating Prediction Approach<br />Evaluation and Results<br />Conclusion and Future Work<br />
    3. 3. Introduction<br />Web 2.0 social software<br />A large amount of user generated content<br />New challenge: selection of useful resources<br />RSS Feeds<br />Pictures<br />Pictures<br />Wiki Pages<br />Documents<br />Videos<br />
    4. 4. Introduction<br />Rating systems<br />Evaluate quality of content in open environment<br />Provide recommendation for different users<br />
    5. 5. Introduction<br />Rating systems – application level<br />Rating systems – academic research level<br />TidalTrust (J. Golbeck), MoleTrust(P. Massa)<br />User explicitly specifies a trust value towards another user<br />Build trust network, Random walk in trust network<br />Personalized rating prediction<br />
    6. 6. Collaborative Learning Domain<br />Collaborative learning environment<br />Unlike e-commerce and review sites<br />Gift economy<br /><ul><li>Rating systems</li></ul>Evaluate user generated content<br />Filter helpful learning resources, peers and group activities<br />Personalized rating prediction for recommendation<br />
    7. 7. 3A Interaction Model<br />
    8. 8. Trust-Based Rating Prediction Approach<br />Objective<br />Build users’ trust network using 3A graph structure<br />Personalize the rating prediction<br />Infer trust value in an implicit way<br /><ul><li>Basic idea</li></ul>What influences rating opinion: similarity and familiarity<br />People tend to trust the opinions of acquaintance and those having similar interests and tastes.<br />
    9. 9. Trust-Based Rating Prediction Approach<br />Trust measurement<br />Multi-relational trust metric<br />Build a “Web of Trust” for a particular user using heterogeneous types of relationships<br /><ul><li>Trust Inference</li></ul>Direct trust<br />Indirect trust<br />Trust<br />How Much?<br />
    10. 10. Trust-Based Rating Prediction Approach<br />Direct trust (DT): derived from a particular type of relationship<br />W (Membership): weight of “membership” relationship<br />N (Alice, Membership): number of group activities Alice joins<br />Is Member of<br />Advanced Algorithms Group Activity<br />Alice<br />
    11. 11. Trust-Based Rating Prediction Approach<br />Trust propagation<br />Propagation distance (PD)<br />Bob<br />Commented by<br />Article<br />Rated by<br />Sara<br />Create<br />Is Member<br />Has Member<br />French Learning Activity<br />Luis<br />Alice<br />Rate<br />Video<br />Rated by<br />Ben<br />Created by<br />Jack<br />Propagate<br />Propagate<br />Propagate<br />PD<br />
    12. 12. Trust-Based Rating Prediction Approach<br />Indirect Trust (IT) Inference<br />
    13. 13. Trust-Based Rating Prediction Approach<br />Rating prediction from a user to an item<br />Using user’s “Web of Trust”<br />People in “Web of Trust” are seen as trustable<br />Average of all the rating scores given by trustable people, weighted by their trust value<br />
    14. 14. Evaluation and Results<br />Using Remashed data set<br />50 users, 6000 items, 3000 tags and 450 ratings<br />“Leave-one-out” method<br />Compare “predicted score – actual score” deviation of trust-based prediction and simple average <br />
    15. 15. Evaluation and Results<br />Change parameters<br />Weights for relationships doesn’t make a significant difference in rating prediction<br />Increasing size of trust network might add noise, lead to bigger prediction error<br />
    16. 16. Conclusion and Future Work<br />Propose a trust-based rating prediction approach, inferring trust in an implicit way<br />Provide personalized rating prediction so as to evaluate user-generated content in collaborative learning environment<br />Future deploy and evaluation will be conducted in a collaborative learning platform, namely Graaasp(graaasp.epfl.ch)<br />
    17. 17. Questions?<br />

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