Title

PerSaDoR: Personalized social document representation for improving web search

Source of Publication

Information Sciences

Abstract

© 2016 Elsevier Inc. In this paper, we discuss a contribution towards the integration of social information in the index structure of an IR system. Since each user has his/her own understanding and point of view of a given document, we propose an approach in which the index model provides a Personalized Social Document Representation (PerSaDoR) of each document per user based on his/her activities in a social tagging system. The proposed approach relies on matrix factorization to compute the PerSaDoR of documents that match a query, at query time. The complexity analysis shows that our approach scales linearly with the number of documents that match the query, and thus, it can scale to very large datasets. PerSaDoR has been also intensively evaluated by an offline study and by a user survey operated on a large public dataset from deliciousshowing significant benefits for personalized search compared to state of the art methods.

Document Type

Article

First Page

614

Last Page

633

Publication Date

11-10-2016

DOI

10.1016/j.ins.2016.07.046

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