Enhancing the Quality of Recommendations through Expert and Trusted Agents

Lorenzi, Fabiana; Abel, Mara; Loh, Stanley; Peres, André

doi:10.1109/ictai.2011.56

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Trust is another interesting concept that addresses which neighbors participate in the recommendation process [15] [ 16], and thus can help to treat the data sparsity problem. This approach is adopted to complement the use of common similarity measures, and is able to increase the target-user neighborhood set by enabling the contribution of users that do not share any common ratings with him or her, but are trustful instead [17] [18].…”

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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