Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Cunha, T (Author) Other; Carlos Soares (Author) FEUP; de Carvalho, ACPLF (Author) Other

Conference proceedings International

Title: DISCOVERY SCIENCE, DS 2017

Pages: 189-203

20th International Conference on Discovery Science (DS)

Kyoto, JAPAN, OCT 15-17, 2017

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00N-083

DOI: 10.1007/978-3-319-67786-6_14

Abstract (EN): Recommender Systems have become increasingly popular, propelling the emergence of several algorithms. As the number of algorithms grows, the selection of the most suitable algorithm for a new task becomes more complex. The development of new Recommender Systems would benefit from tools to support the selection of the most suitable algorithm. Metalearning has been used for similar purposes in other tasks, such as classification and regression. It learns predictive models to map characteristics of a dataset with the predictive performance obtained by a set of algorithms. For such, different types of characteristics have been proposed: statistical and/or information-theoretical, model-based and landmarkers. Recent studies argue that landmarkers are successful in selecting algorithms for different tasks. We propose a set of landmarkers for a Metalearning approach to the selection of Collaborative Filtering algorithms. The performance is compared with a state of the art systematic metafeatures approach using statistical and/or information-theoretical metafeatures. The results show that the metalevel accuracy performance using landmarkers is not statistically significantly better than the metafeatures obtained with a more traditional approach. Furthermore, the baselevel results obtained with the algorithms recommended using landmarkers are worse than the ones obtained with the other metafeatures. In summary, our results show that, contrary to the results obtained in other tasks, these landmarkers are not necessarily the best metafeatures for algorithm selection in Collaborative Filtering.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15