Publications

bias  collaborative filtering  collaborative systems  contact recommendation  context  diversity  evaluation  experimental methodology  expermiental methodology  false positive  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  network management  news retrieval  nlp  non-random missing data  novelty  performance prediction  personalization  popularity  rank aggregation  rank fusion  recommendation  recommender systems  relation extraction  semantic search  semantics  social networks  web services 

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2020

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.

(BibTeX | Tags: recommender systems, metrics, evaluation, popularity, bias, non-random missing data, false positive)

2018

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.
Poster

(BibTeX | Tags: recommender systems, evaluation, popularity, information retrieval theory, bias, non-random missing data)
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.
Slides

(BibTeX | Tags: recommender systems, evaluation, information retrieval, popularity, information retrieval theory, bias, non-random missing data)
R. Cañamares, P. Castells. From the PRP to the Low Prior Discovery Recall Principle for Recommender Systems. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). Ann Arbor, Michigan, USA. July 2018, pp. 1081-1084.
Poster

(BibTeX | Tags: novelty, recommender systems, metrics, evaluation, information retrieval, popularity, information retrieval theory)

2017

R. Cañamares, P. Castells. On the Optimal Non-Personalized Recommendation: From the PRP to the Discovery False Negative Principle. ACM SIGIR 2017 Workshop on Axiomatic Thinking for Information Retrieval and Related Tasks (ATIR 2017). Tokyo, Japan, August 2017.

(BibTeX | Tags: recommender systems, evaluation, popularity, information retrieval theory, probability ranking principle)
R. Cañamares, P. Castells. A Probabilistic Reformulation of Memory-Based Collaborative Filtering – Implications on Popularity Biases. 40th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017). Tokyo, Japan, August 2017, pp. 215-224.
Slides

(BibTeX | Tags: evaluation, collaborative filtering, knn, popularity, probabilistic models)
A. Bellogín, P. Castells, I. Cantador. Statistical Biases in Information Retrieval Metrics for Recommender Systems. Information Retrieval 20(6), July 2017, pp. 606-634.

(BibTeX | Tags: recommender systems, evaluation, popularity)

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