CF4CF-META: Hybrid Collaborative Filtering Algorithm Selection Framework

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Tiago Cunha (Author) Other; Carlos Soares (Author) FEUP; André C. P. L. F. de Carvalho (Author) Other

Conference proceedings International

Title: Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings

Pages: 114-128

21st International Conference on Discovery Science, DS 2018

29 October 2018 through 31 October 2018

Indexing

Scopus - 1 Citation

INSPEC

Other information

Authenticus ID: P-00P-Q3S

DOI: 10.1007/978-3-030-01771-2_8

Abstract (EN): The algorithm selection problem refers to the ability to predict the best algorithms for a new problem. This task has been often addressed by Metalearning, which looks for a function able to map problem characteristics to the performance of a set of algorithms. In the context of Collaborative Filtering, a few studies have proposed and validated the merits of different types of problem characteristics for this problem (i.e. dataset-based approach): using systematic metafeatures and performance estimations obtained by subsampling landmarkers. More recently, the problem was tackled using Collaborative Filtering models in a novel framework named CF4CF. This framework leverages the performance estimations as ratings in order to select the best algorithms without using any data characteristics (i.e algorithm-based approach). Given the good results obtained independently using each approach, this paper starts with the hypothesis that the integration of both approaches in a unified algorithm selection framework can improve the predictive performance. Hence, this work introduces CF4CF-META, an hybrid framework which leverages both data and algorithm ratings within a modified Label Ranking model. Furthermore, it takes advantage of CF4CF¿s internal mechanism to use samples of data at prediction time, which has proven to be effective. This work starts by explaining and formalizing state of the art Collaborative Filtering algorithm selection frameworks (Metalearning, CF4CF and CF4CF-META) and assess their performance via an empirical study. The results show CF4CF-META is able to consistently outperform all other frameworks with statistically significant differences in terms of meta-accuracy and requires fewer landmarkers to do so. © 2018, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15