Modeling Temporal Behavior of Awards Effect on Viewership of Movies

Handle URI:
http://hdl.handle.net/10754/623318
Title:
Modeling Temporal Behavior of Awards Effect on Viewership of Movies
Authors:
Altaf, Basmah; Kamiran, Faisal; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
The “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.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Citation:
Altaf 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.
Publisher:
Springer International Publishing
Journal:
Advances in Knowledge Discovery and Data Mining
Issue Date:
22-Apr-2017
DOI:
10.1007/978-3-319-57454-7_56
Type:
Book Chapter
ISSN:
0302-9743; 1611-3349
Additional Links:
http://link.springer.com/chapter/10.1007%2F978-3-319-57454-7_56
Appears in Collections:
Book Chapters

Full metadata record

DC FieldValue Language
dc.contributor.authorAltaf, Basmahen
dc.contributor.authorKamiran, Faisalen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2017-05-04T06:33:44Z-
dc.date.available2017-05-04T06:33:44Z-
dc.date.issued2017-04-22en
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.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-319-57454-7_56en
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.en
dc.publisherSpringer International Publishingen
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-319-57454-7_56en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-57454-7_56en
dc.subjectAwards effect estimation, Popularity bias, Recommender systemsen
dc.titleModeling Temporal Behavior of Awards Effect on Viewership of Moviesen
dc.typeBook Chapteren
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalAdvances in Knowledge Discovery and Data Miningen
dc.eprint.versionPost-printen
dc.contributor.institutionInformation Technology, University of the Punjab, Lahore, Pakistanen
kaust.authorAltaf, Basmahen
kaust.authorZhang, Xiangliangen
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