Multi-instance genetic programming for web index recommendation

Dept. of Computer Sciences and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
Expert Systems with Applications (Impact Factor: 1.97). 11/2009; 36(9):11470-11479. DOI: 10.1016/j.eswa.2009.03.059

ABSTRACT This article introduces the use of a multi-instance genetic programming algorithm for modelling user preferences in web index recommendation systems. The developed algorithm learns user interest by means of rules which add comprehensibility and clarity to the discovered models and increase the quality of the recommendations. This new model, called G3P-MI algorithm, is evaluated and compared with other available algorithms. Computational experiments show that our methodology achieves competitive results and provide high-quality user models which improve the accuracy of recommendations.

Download full-text


Available from: Sebastian Ventura, Jun 19, 2015
1 Follower
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Web index recommendation systems are designed to help internet users with suggestions for finding relevant information. One way to develop such systems is using the multi-instance learning (MIL) approach: a generalization of the traditional supervised learning where each example is a labeled bag that is composed of unlabeled instances, and the task is to predict the labels of unseen bags. This paper proposes a multi-instance learning wrapper method using the Rocchio classifier to recommend web index pages. The wrapper implements a new way to relate the instances with the class labels of the bags. The proposed method has low computational cost and the experimental study on benchmark data sets shows that it performs better than the state-of-the-art methods for this problem.
    Knowledge-Based Systems 03/2014; 59. DOI:10.1016/j.knosys.2014.01.008 · 3.06 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to find efficient solutions when traditional supervised learning is used. To check the efficiency of the new proposed representation, the most popular techniques of traditional supervised learning based on single instances are compared to those based on multiple instance learning. Computational experiments show that when the problem is regarded as a multiple instance one, performance is significantly better and the weaknesses of single-instance representation are overcome.
    Expert Systems with Applications 11/2011; 38(12):15020-15031. DOI:10.1016/j.eswa.2011.05.044 · 1.97 Impact Factor
  • Source
    Expert Systems with Applications 01/2011; 30. · 1.97 Impact Factor