Social context based recommendation systems and trust inference
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Social Context Based Recommendation Systems and Trust Inference. Student: Andrea Manrique ID: 41448529. Advisor: A/Prof. Yan Wang Macquarie University November 2011. Agenda. Introduction Review of Recommender Systems (RSs) What RSs are ? Traditional RSs Disadvantages of current RSs

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Social Context Based Recommendation Systems and Trust Inference

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Social context based recommendation systems and trust inference

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


Agenda

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


Introduction

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


Agenda1

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


Recommender systems review

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


Rss traditional rss

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


Rss the new generation

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


Rss the new generation1

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


Agenda2

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


Review of social context aware in rss

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


Online social networks osn

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


Agenda3

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


Review of trust inference in social networks

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


What is trust inference

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


Future work

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


Conclusion

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


Questions

Questions?

ITEC810, Macquarie University


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