Exploiting Performance Estimates for Augmenting Recommendation Ensembles

Author:

Penha Gustavo1,Santos Rodrygo L. T.2

Affiliation:

1. Delft University of Technology, Netherlands

2. Department of Computer Science Universidade Federal de Minas Gerais, Brazil

Publisher

ACM

Reference32 articles.

1. Recommender Systems

2. Recommender Systems

3. Javed A Aslam and Mark Montague. 2001. Models for metasearch. In SIGIR. ACM 276–284. Javed A Aslam and Mark Montague. 2001. Models for metasearch. In SIGIR. ACM 276–284.

4. Xinlong Bao Lawrence Bergman and Rich Thompson. 2009. Stacking recommendation engines with additional meta-features. In RecSys. ACM 109–116. Xinlong Bao Lawrence Bergman and Rich Thompson. 2009. Stacking recommendation engines with additional meta-features. In RecSys. ACM 109–116.

5. Ariel Bar Lior Rokach Guy Shani Bracha Shapira and Alon Schclar. 2012. Boosting simple collaborative filtering models using ensemble methods. arXiv preprint arXiv:1211.2891(2012). Ariel Bar Lior Rokach Guy Shani Bracha Shapira and Alon Schclar. 2012. Boosting simple collaborative filtering models using ensemble methods. arXiv preprint arXiv:1211.2891(2012).

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1. Dynamic weighted ensemble learning for sequential recommendation systems: The AIRE model;Future Generation Computer Systems;2023-12

2. Collaborative filtering algorithms are prone to mainstream-taste bias;Proceedings of the 17th ACM Conference on Recommender Systems;2023-09-14

3. Enhancing recommender ensemble by estimating input fitness;Computers and Electrical Engineering;2022-12

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