PhD Thesis 2008

Title: Relevance Models for Collaborative Filtering

defended April 2008

Doctoral Consortium Award, SIGIR2006



Collaborative filtering is the common technique of predicting the
interests of a user by collecting preference information from many
users. Although it is generally regarded as a key information
retrieval technique, its relation to the existing information
retrieval theory is unclear. This thesis shows how the development of
collaborative filtering can gain many benefits from information
retrieval theories and models. It brings the notion of relevance into
collaborative filtering and develops several relevance models for
collaborative filtering. Besides dealing with user profiles that are
obtained by explicitly asking users to rate information items, the
relevance models can also cope with the situations where user profiles
are implicitly supplied by observing user interactions with a
system. Experimental results complement the theoretical insights with
improved recommendation accuracy for both item relevance ranking and
user rating prediction. Furthermore, the approaches are more than just
analogy: our derivations of the unified relevance model show that
popular user-based and item-based approaches represent only a partial
view of the problem, whereas a unified view that brings these partial
views together gives better insights into their relative importance
and how retrieval can benefit from their combination.

A PDF version can be downloaded from here

author = {Jun Wang},
title =  {Relevance Models for Collaborative Filtering},
school =  {Delft University of Technology},
year =  {2008},
address = {Delft, The Netherlands},
month = {April},
url = {},

Jun Wang, 2008

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