https://doi.org/10.31979/etd.mx3m-usdr">

Publication Date

2008

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

Abstract

Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset.

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