Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
Interest
Topics
Details

Details

001
Publications

2018

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

Authors
Cunha, T; Soares, C; de Carvalho, ACPLF;

Publication
INFORMATION SCIENCES

Abstract
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.

2018

A label ranking approach for selecting rankings of collaborative filtering algorithms

Authors
Cunha, T; Soares, C; de Carvalho, ACPLF;

Publication
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, April 09-13, 2018

Abstract
The large amount of Recommender System algorithms makes the selection of the most suitable algorithm for a new dataset a difficult task. Metalearning has been successfully used to deal with this problem. It works by mapping dataset characteristics with the predictive performance obtained by a set of algorithms. The models built on this data are capable of predicting the best algorithm for a new dataset. However, typical approaches try only to predict the best algorithm, overlooking the performance of others. This study focus on the use of Metalearning to select the best ranking of CF algorithms for a new recommendation dataset. The contribution lies in the formalization and experimental validation of using Label Ranking to select a ranked list of algorithms. The experimental procedure proves the superior performance of the proposed approach regarding both ranking accuracy and impact on the baselevel performance. Furthermore, it draws and compares the knowledge regarding metafeature importance for both classification and Label Ranking tasks in order to provide guidelines for the design of algorithms in the Recommender System community. © Copyright held by the owner/author(s).

2017

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

Authors
Cunha, T; Soares, C; de Carvalho, ACPLF;

Publication
Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27-31, 2017

Abstract
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. © 2017 ACM.

2017

Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers

Authors
Cunha, T; Soares, C; de Carvalho, ACPLF;

Publication
Discovery Science - 20th International Conference, DS 2017, Kyoto, Japan, October 15-17, 2017, Proceedings

Abstract

2016

Selecting Collaborative Filtering Algorithms Using Metalearning

Authors
Cunha, T; Soares, C; Carvalho, ACPLFd;

Publication
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II

Abstract
Recommender Systems are an important tool in e-business, for both companies and customers. Several algorithms are available to developers, however, there is little guidance concerning which is the best algorithm for a specific recommendation problem. In this study, a metalearning approach is proposed to address this issue. It consists of relating the characteristics of problems (metafeatures) to the performance of recommendation algorithms. We propose a set of metafeatures based on the application of systematic procedure to develop metafeatures and by extending and generalizing the state of the art metafeatures for recommender systems. The approach is tested on a set of Matrix Factorization algorithms and a collection of real-world Collaborative Filtering datasets. The performance of these algorithms in these datasets is evaluated using several standard metrics. The algorithm selection problem is formulated as classification tasks, where the target attribute is the best Matrix Factorization algorithm, according to each metric. The results show that the approach is viable and that the metafeatures used contain information that is useful to predict the best algorithm for a dataset. © Springer International Publishing AG 2016.

2016

AToMRS: A Tool to Monitor Recommender Systems

Authors
Costa, A; Cunha, T; Soares, C;

Publication
KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1

Abstract
Recommender systems arose in response to the excess of available online information. These systems assign, to a given individual, suggestions of items that may be relevant. These system's monitoring and evaluation are fundamental to the proper functioning of many business related services. It is the goal of this paper to create a tool capable of collecting, aggregating and supervising the results obtained from the recommendation systems' evaluation. To achieve this goal, a multi-granularity approach is developed and implemented in order to organize the different levels of the problem. This tool also aims to tackle the lack of mechanisms to enable visually assessment of the performance of a recommender systems' algorithm. A functional prototype of the application is presented, with the purpose of validating the solution's concept.