Improving Web Search using Trust and Social Networks
This event took place on Wednesday 25 October 2006 at 11:30
Tom Heath KMi, The Open University
Conventional search engines treat all users the same. Relevance is seen as a relationship between a query and a resource, ignoring aspects of the user's information need that are not explicit in the query. This contrasts with offline information seeking, where people frequently use social networks of known individuals as a source of information and as a basis for assessing its relevance. In this presentation I will outline our approach to personalised information seeking, based on computing trust relationships between the user and members of their social networks as a means to rank and filter resources. Results of an empirical study underlying this approach will be presented, followed by a demonstration of parts of the infrastructure through which our approach will be realised.
This event took place on Wednesday 25 October 2006 at 11:30
Conventional search engines treat all users the same. Relevance is seen as a relationship between a query and a resource, ignoring aspects of the user's information need that are not explicit in the query. This contrasts with offline information seeking, where people frequently use social networks of known individuals as a source of information and as a basis for assessing its relevance. In this presentation I will outline our approach to personalised information seeking, based on computing trust relationships between the user and members of their social networks as a means to rank and filter resources. Results of an empirical study underlying this approach will be presented, followed by a demonstration of parts of the infrastructure through which our approach will be realised.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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