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This paper presents a stochastic model based on Monte Carlo simulation techniques for measuring the performance of recommenders. A general procedure to assess the accuracy of recommendation predictions is presented and implemented in a typical case study where input parameters are treated as random values and recommender errors are estimated using sensitive analysis. The results obtained are presented and a new perspective to the evaluation and assessment of recommender systems is discussed.

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@inproceedings{DBLP:conf/um/CapurucoC12, author = {Renato A. C. Capuru\c{c}o and Luiz Fernando Capretz}, title = {A fuzzy-based inference mechanism of trust for improved social recommenders}, booktitle = {UMAP Workshops}, year = {2012}, ee = {http://ceur-ws.org/Vol-872/srs2012_paper_3.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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