bias collaborative filtering collaborative systems contact recommendation context diversity ensembles evaluation false positive false positives finance group recommendation hybrid recommendation information extraction information retrieval information retrieval models information retrieval theory knn link prediction metasearch metrics multi-armed bandits multimedia retrieval news retrieval nlp non-random missing data novelty performance prediction personalization popularity probability ranking principle rank aggregation rank fusion recommender systems relation extraction semantic annotation semantic search semantics social networks web services
|E. Mena-Maldonado, R. Cañamares, P. Castells, Y. Ren, M. Sanderson. Popularity Bias in False-Positive Metrics for Recommender Systems Evaluation. ACM Transactions on Information Systems 39(3), May 2021.|
|R. Cañamares, P. Castells. On Target Item Sampling in Offline Recommender System Evaluation. 14th ACM Conference on Recommender Systems (RecSys 2020). Virtual Event, Brazil, September 2020, pp. 259–268.|
|E. Mena-Maldonado, R. Cañamares, P. Castells, Y. Ren, M. Sanderson. Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020). Virtual Event, China, July 2020, pp. 841-850.|
P. Castells, R. Cañamares.
Characterization of Fair Experiments for Recommender System Evaluation – A Formal Analysis.
Workshop on Offline Evaluation for Recommender Systems (REVEAL 2018) at the 12th ACM Conference on Recommender Systems (RecSys 2018). Vancouver, Canada, October 2018.
R. Cañamares, P. Castells.
Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems.
41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). Ann Arbor, Michigan, USA, July 2018, pp. 415-424 — best paper award.