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Evaluation and Assessment of Recommenders Using Monte Carlo Simulation
Electrical and Computer Engineering Publications
  • Renato Costa, University of Western Ontario
  • Luiz Fernando Capretz, University of Western Ontario
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There have been various definitions, representations and derivations of trust in the context of recommender systems. This article presents a recommender predictive model based on collaborative filtering techniques that incorporate a fuzzy-driven quantifier, which includes two upmost relevant social phenomena parameters to address the vagueness inherent in the assessment of trust in social networks relationships. An experimental evaluation procedure utilizing a case study is conducted to analyze the overall predictive accuracy. These results show that the proposed methodology improves the performance of classical recommender approaches. Possible extensions are then outlined.

Citation Information
@inproceedings{DBLP:conf/um/CapurucoC12a, author = {Renato A. C. Capuru\c{c}o and Luiz Fernando Capretz}, title = {Evaluation and assessment of recommenders using Monte Carlo simulation}, booktitle = {UMAP Workshops}, year = {2012}, ee = {}, crossref = {DBLP:conf/um/2012w}, bibsource = {DBLP,} } @proceedings{DBLP:conf/um/2012w, editor = {Eelco Herder and Kalina Yacef and Li Chen and Stephan Weibelzahl}, title = {Workshop and Poster Proceedings of the 20th Conference on User Modeling, Adaptation, and Personalization, Montreal, Canada, July 16-20, 2012}, booktitle = {UMAP Workshops}, publisher = {}, series = {CEUR Workshop Proceedings}, volume = {872}, year = {2012}, ee = {}, bibsource = {DBLP,} }