Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2984
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dc.contributor.authorSreepada, Rama Syamala-
dc.contributor.authorPatra, Bidyut Kumar-
dc.date.accessioned2018-04-17T04:22:34Z-
dc.date.available2018-04-17T04:22:34Z-
dc.date.issued2018-03-
dc.identifier.citation40th European Conference on Information Retrieval (ECIR 2018), Grenoble, France, 26- 29 March, 2018.en_US
dc.identifier.urihttp://hdl.handle.net/2080/2984-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThe crux of a recommendation engine is to process users ratings and provide personalized suggestions to the user. However, processing the ratings and providing recommendations in real time still remains challenging, when there is a perpetual influx of new ratings. Traditional approaches fail to accommodate the new streamlined ratings and update the users' preferences on the y. In this paper, we address this challenge of streaming data without compromising accuracy and efficiency of recommender system. We identify the affected users and incrementally update their vital statistics after each new rating. We propose an incremental similarity measure for fi nding neighbors who play an important role in personalizing recommendations for active user. Experimental results on real-world datasets show that the proposed approach outperforms the state-of-the-art techniques in terms of accuracy and execution time.en_US
dc.subjectCollaborative lteringen_US
dc.subjectPersonalized recommendationen_US
dc.subjectStream- lined ratingsen_US
dc.subjectTendency based approachen_US
dc.subjectIncremental updatesen_US
dc.titleAn Incremental Approach for Collaborative Filtering in Streaming Scenariosen_US
dc.typeArticleen_US
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