Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering

• Type: Another Publication in an International Scientific Journal
• Date: 2018

Authors

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

Journal

Title: Information Sciences

Vol. 423

Pages: 128-144

ISSN: 0020-0255

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 54 Citations

Scopus - 79 Citations

Other information

Authenticus ID: P-00N-1N7

DOI: 10.1016/j.ins.2017.09.050

Abstract (EN): The problem of information overload motivated the appearance of Recommender Systems. From the several open problems in this area, the decision of which is the best recommendation algorithm for a specific problem is one of the most important and less studied. The current trend to solve this problem is the experimental evaluation of several recommendation algorithms in a handful of datasets. However, these studies require an extensive amount of computational resources, particularly processing time. To avoid these drawbacks, researchers have investigated the use of Metalearning to select the best recommendation algorithms in different scopes. Such studies allow to understand the relationships between data characteristics and the relative performance of recommendation algorithms, which can be used to select the best algorithm(s) for a new problem. The contributions of this study are two-fold: 1) to identify and discuss the key concepts of algorithm selection for recommendation algorithms via a systematic literature review and 2) to perform an experimental study on the Metalearning approaches reviewed in order to identify the most promising concepts for automatic selection of recommendation algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17