NDLI: Feature enhanced time-aware recommender system

Content Provider	IEEE Xplore Digital Library
Author	Karahodza, B. Donko, D.
Copyright Year	2015
Description	Author affiliation: Fac. of Electr. Eng., Univ. in Sarajevo, Sarajevo, Bosnia-Herzegovina (Karahodza, B.; Donko, D.)
Abstract	Traditional recommender systems utilize user and item profiles in order to predict ratings of unseen items. New users, items and ratings are continuously updated to the system, making data available for detection of changes in user preferences throughout the time. In this work the widely used user-neighborhood recommender system is extended by incorporating temporal information and enhancing measure of neighborhood similarity with information on item features. Unlike other models, we also add time-weight function in the preference prediction step to improve prediction accuracy. Experiments on real data set show an improvement in prediction performance over traditional collaborative filtering model.
Starting Page	1
Ending Page	6
File Size	243701
Page Count	6
File Format	PDF
e-ISBN	9781467381468
DOI	10.1109/ICAT.2015.7340527
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-29
Publisher Place	Bosnia Herzegovina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Collaborative filtering Collaboration Machine learning Predictive models Market research Data models Information filtering Mathematical model Data mining Recommender systems
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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