Metalearning for Context-aware Filtering: Selection of Tensor Factorization Algorithms

• 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: Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17

Pages: 14-22

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00M-YFG

DOI: 10.1145/3109859.3109899

Abstract (EN): This work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. The most important challenge of applying metalearning on new problems is the development of useful measures able to characterize the data, i.e. metafeatures. We propose an extensive and exhaustive set of metafeatures to characterize Context-aware Filtering recommendation task. These metafeatures take advantage of the tensor's hierarchical structure via slice operations. The algorithm selection task is addressed as a Label Ranking problem, which ranks the Tensor Factorization algorithms according to their expected performance, rather than simply selecting the algorithm that is expected to obtain the best performance. A comprehensive experimental work is conducted on both levels, baselevel and metalevel (Tensor Factorization and Label Ranking, respectively). The results show that the proposed metafeatures lead to metamodels that tend to rank Tensor Factorization algorithms accurately and that the selected algorithms present high recommendation performance.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9