Ranking Oriented Algorithms for Context Aware Recommendation

PhD Thesis Proposal Defence


Title: "Ranking Oriented Algorithms for Context Aware Recommendation"

by

Mr. Nan LIU


ABSTRACT:

Recommender systems have become increasingly important due to the
ubiquity of information overload across various application domains.
Unlike search systems in which the user would specify their
information need, recommender systems have to infer user's information
needs from observed user activities in order to help user discovery
interesting and novel items. As the technology and application of
recommendation is rapidly evolving in these years, traditional
collaborative filtering algorithms such as nearest neighbor or matrix
factorization have fallen short in coping with several emerging but
critical issues in modern systems. Firstly, ranking items, especially
identifying a few most interesting items out of a huge pool, has
become the core task in most application scenarios. However,
traditional algorithms focus on doing regression on the observed user
ratings (i.e., explicit user feedback), which is a detour towards the
end goal of ranking. In this work, we propose a new framework for
directly solving the personalized ranking problem by representing user
feedback using pairwise preference based representation. Secondly,
modern systems collect user feedback in more diverse forms(e.g.,
rating, click, browse, purchases) whereas existing methods only handle
explicit feedback. To cope with data sparsity, it is necessary to
integrate multiple sources of information. In this work, we show that
ranking models are also an effective way to unify the heterogeneous
representations of different forms of user feedback. Finally,
traditional recommendation algorithms do not take into account
contextual factors such as time, location and social networks, which
are nevertheless very important as recommender systems are becoming
part of people's daily life and being accessed on both desktop and
mobile platforms. We therefore also try to develop recommendation
models that could incorporate time and social network information.


Date:                   Wednesday, 31 August 2011

Time:                   1:00pm - 3:00pm

Venue:                  Room 3501
                         lifts 25/26

Committee Members:      Prof. Qiang Yang (Supervisor)
                         Dr. Sunghun Kim (Chairperson)
 			Prof. Dik-Lun Lee
 			Dr. Raymond Wong


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