Social Context Based Recommendation Systems and Trust Inference

1. Social Context Based Recommendation Systems and Trust Inference Student: Andrea Manrique ID: 41448529 • Advisor:A/Prof. Yan Wang • Macquarie University November 2011 ITEC810, Macquarie University

2. Agenda • Introduction • Review of Recommender Systems (RSs) • What RSs are? • Traditional RSs • Disadvantages of current RSs • The New Generation of RSs • Review of Social Context Aware in RSs • Review of Trust Inference in Social Networks • Future Work • Conclusions ITEC810, Macquarie University

3. Introduction • Recommender System have been gaining importance in many areas • Exponential growth of Online Social Networks (OSN) • Traditional RSs do not consider social context impact • Need of trustworthiness in recommendations • Broader range of factors that motivate people in their decision making ITEC810, Macquarie University

4. Agenda • Introduction • Review of Recommender Systems (RSs) • What RSs are? • Traditional RSs • Disadvantages of current RSs • The New Generation of RSs • Review of Social-Context Aware • Review of Trust Inference in Social Networks • Future Work • Conclusions ITEC810, Macquarie University

5. Recommender Systems Review • What is a RS? • A system that seeks to provide recommendations about items that may be of interest to a user. (Bonhard, 2004) ITEC810, Macquarie University

6. RSs - Traditional RSs • Typically based on collaborative filtering • Automatically predicts the interest of an active user by collecting rating information from other similar users or items • Approaches: • Collaborative filtering • Content-based Filtering • Hybrid filtering • Disadvantages of current RSs ITEC810, Macquarie University

7. RSs – The new generation • Online Social Networks are online communities where people participate and are connected by a set of social relationships. • Social context (particularly social relationships among users) is ignored by traditional recommender systems. • Trust gives users information about the people they interact, sharing or receiving content ITEC810, Macquarie University

8. RSs – The new generation • Trust-Aware RSs • There is a significant correlation between the trust expressed by the users and their similarity based on the recommendations they made in the system. • “The more similar two people are, the greater the trust between them” (Golbeck, 2006) • Social RSs • Incorporate users’ social network information to improve recommendations. ITEC810, Macquarie University

9. Agenda • Introduction • Review of Recommender Systems (RSs) • What RSs are? • Traditional RSs • Disadvantages of current RSs • The New Generation of RSs • Review of Social-Context Aware • Review of Trust Inference in Social Networks • Future Work • Conclusions ITEC810, Macquarie University

10. Review of Social Context Aware in RSs • Definition: A general definition of Social Context would be the social aspects of the current user context. • Web 2.0 applications RSs are now associated with various kinds of social contextual information. • Trusted friends are seen as more qualified to make good and useful recommendations compared to traditional RSs ITEC810, Macquarie University

11. Online Social Networks (OSN) • Definition: An Online Social Network is a website that facilitates meeting people, finding like minds, communicating and sharing content, and building a community (Zhou, Xu, Li, Josang & Cox, 2011). • The exponential growth posses new challenges for traditional RSs • In some OSNs, users can express how much they trust other users. ITEC810, Macquarie University

12. Agenda • Introduction • Review of Recommender Systems (RSs) • What RSs are? • Traditional RSs • Disadvantages of current RSs • The New Generation of RSs • Review of Social-Context Aware • Review of Trust Inference in social networks • Future Work • Conclusions ITEC810, Macquarie University

13. Review of Trust Inference in social networks • Trust between participants in social networks can be defined as “the rely on one participant in another, based on their interaction” • Trust between users in social networks indicates similarity in their opinions (Ziegler & Golbeck, 2006). • Incorporating trust, recommender systems can be more effective than systems based on traditional techniques like collaborative filtering (Massa & Avesani, 2004) ITEC810, Macquarie University

14. What is trust inference? • Trust inference could be defined as the approach that seeks to find out how much a user should trust another one in a network. • The goal of trust inference is to infer an accurate trust value that could exist between two people without direct connection • The user might look for information from others who are not directly connected to him. • Users can make decisions based on this trust value of other But, why trust inference is important in RSs? ITEC810, Macquarie University

15. Future Work • Deeper study of the complicated nature of social human-to-human interaction which comes into play when recommending people. • The design and development of more interactive and richer recommender system user interfaces. • Scalability and efficiency of algorithms, when the social graph grows with uncountable nodes. ITEC810, Macquarie University

16. Conclusion • The inclusion of social contextual information makes an important contribution to the personalization of the recommendation by itself, improving its accuracy and quality. In this scenario, if trusted users replace the neighbors used in traditional recommender systems, then it is possible to assure reliable and accurate recommendations, avoiding some inefficient processes still presented in traditional approaches. ITEC810, Macquarie University

17. Questions? ITEC810, Macquarie University