NDLI: GAPfm: optimal top-n recommendations for graded relevance domains

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GAPfm: optimal top-n recommendations for graded relevance domains

Content Provider	ACM Digital Library
Author	Shi, Yue Karatzoglou, Alexandros Baltrunas, Linas Larson, Martha Hanjalic, Alan
Abstract	Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. Current ranking techniques are based on one of two sub-optimal approaches: either they optimize for a binary metric such as Average Precision, which discards information on relevance levels, or they optimize for Normalized Discounted Cumulative Gain (NDCG), which ignores the dependence of an item's contribution on the relevance of more highly ranked items. We address the shortcomings of existing approaches by proposing GAPfm, the Graded Average Precision factor model, which is a latent factor model for top-N recommendation in domains with graded relevance data. The model optimizes the Graded Average Precision metric that has been proposed recently for assessing the quality of ranked results lists for graded relevance. GAPfm's advantages are twofold: it maintains full information about graded relevance and also addresses the limitations of models that optimize NDCG. Experimental results show that GAPfm achieves substantial improvements on the top-N recommendation task, compared to several state-of-the-art approaches.
Starting Page	2261
Ending Page	2266
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505653
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ranking Collaborative filtering Latent factor model Top-n recommendation Graded average precision Recommender systems
Content Type	Text
Resource Type	Article

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