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dc.contributor.authorAltaf, Basmah
dc.contributor.authorKamiran, Faisal
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2017-05-04T06:33:44Z
dc.date.available2017-05-04T06:33:44Z
dc.date.issued2017-04-22
dc.identifier.citationAltaf B, Kamiran F, Zhang X (2017) Modeling Temporal Behavior of Awards Effect on Viewership of Movies. Lecture Notes in Computer Science: 724–736. Available: http://dx.doi.org/10.1007/978-3-319-57454-7_56.
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.doi10.1007/978-3-319-57454-7_56
dc.identifier.urihttp://hdl.handle.net/10754/623318
dc.description.abstractThe “rich get richer” effect is well-known in recommendation system. Popular items are recommended more, then purchased more, resulting in becoming even more popular over time. For example, we observe in Netflix data that awarded movies are more popular than non-awarded movies. Unlike other work focusing on making fair/neutralized recommendation, in this paper, we target on modeling the effect of awards on the viewership of movies. The main challenge of building such a model is that the effect on popularity changes over time with different intensity from movie to movie. Our proposed approach explicitly models the award effects for each movie and enables the recommendation system to provide a better ranked list of recommended movies. The results of an extensive empirical validation on Netflix and MovieLens data demonstrate the effectiveness of our model.
dc.publisherSpringer International Publishing
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-319-57454-7_56
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-57454-7_56
dc.subjectAwards effect estimation, Popularity bias, Recommender systems
dc.titleModeling Temporal Behavior of Awards Effect on Viewership of Movies
dc.typeBook Chapter
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalAdvances in Knowledge Discovery and Data Mining
dc.eprint.versionPost-print
dc.contributor.institutionInformation Technology, University of the Punjab, Lahore, Pakistan
kaust.personAltaf, Basmah
kaust.personZhang, Xiangliang
refterms.dateFOA2018-04-23T00:00:00Z


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