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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.

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@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 = {http://ceur-ws.org/Vol-872/srs2012_paper_4.pdf}, crossref = {DBLP:conf/um/2012w}, bibsource = {DBLP, http://dblp.uni-trier.de} } @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 = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, volume = {872}, year = {2012}, ee = {http://ceur-ws.org/Vol-872}, bibsource = {DBLP, http://dblp.uni-trier.de} }

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