NDLI: A collaborative filtering framework based on local and global similarities with similarity tie-breaking criteria

Content Provider	IEEE Xplore Digital Library
Author	Lopes, A.R.S. Prudencio, R.B.C. Bezerra, B.L.D.
Copyright Year	2014
Description	Author affiliation: Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil (Lopes, A.R.S.; Prudencio, R.B.C.) \|\| Polytech. Sch. of Pernambuco, Univ. of Pernambuco, Recife, Brazil (Bezerra, B.L.D.)
Abstract	Collaborative Filtering is the most commonly used technique in Recommender Systems, based on the users ratings in order to identify similar profiles and suggest them items. However, because it depends essentially on direct similarity measures between users or items, it usually suffers from the sparsity problem. Upon this situation, a good alternative is using global similarities to enrich the users neighborhood by transitively connecting them together, even when they do not share any common ratings. In this paper, we investigated the use of both local and global similarity measures with the maximin distance algorithm, along with tie-breaking criteria for neighbors with equal similarity. Our experiments showed that the maximin distance algorithm in fact produces many equally similar global neighbors, and that the criteria set for deciding between them severely improved the results of the recommendation process.
Starting Page	2887
Ending Page	2893
File Size	452551
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889418
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Recommender systems Collaboration Accuracy Vectors Prediction algorithms Measurement
Content Type	Text
Resource Type	Article

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