A recommender system architecture for an inter-application environment

SANTOS, EUNICE LIMA DOS; Bezerra, Byron Leite Dantas; Amorim, Jefferson Silva de; Nascimento, Arthur Inacio do

doi:10.1109/isda.2012.6416584

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A Data Sparsity1

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2014

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“…As realworld systems usually have a really big number of users and items, even the most active users consume just a few items of the whole database [6]. Similarly, even the most popular items are rated by just a small portion of the users in the system.…”

Section: A Data Sparsitymentioning

confidence: 99%

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

Lopes

Prudêncio

Bezerra

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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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.

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Section: A Data Sparsitymentioning

confidence: 99%