Mining Relational Context-aware Graph for Rater Identification (10% error rate for Rater Identification Track)

TitleMining Relational Context-aware Graph for Rater Identification (10% error rate for Rater Identification Track)
Publication TypeConference Paper
Year of Publication2011
AuthorsShi, Y, Larson, MA, Hanjalic, A
Refereed DesignationRefereed
Conference NameCAMRa2011: Proceedings of the RecSys ’11 Challenge on Context-aware Movie Recommendation
Pagination53-59
PublisherACM
Conference LocationChicago, USA
ISBN Number978-1-4503-0825-0
Keywordscontext-aware recommendation, graph, random walk with restart
Abstract

This paper studies the rater identification problem in recommender systems. We propose to approach rater identification by fusing influence from various factors that relate to users. The rater likelihood is modeled from a probabilistic point of view as the conditional probability of a user given sources of contextual information, resulting in an aggregation model that fuses all the available information sources pertaining to a particular user. The result is a relational context-aware graph. A random walk with restart is used to calculate the proximity scores over this graph, which are used to identify raters. We compare our approach with several baselines in a set of experiments performed on the CAMRa2011 challenge dataset. The results demonstrate the superiority of our approach in predicting the identity of a rater who rated a particular movie within a given household.

URLhttp://dl.acm.org/citation.cfm?id=2096122&CFID=62887899&CFTOKEN=11787779
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