Multi-instance genetic programming for web index recommendation

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.

[1]  Yann Chevaleyre,et al.  Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem , 2001, Canadian Conference on AI.

[2]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[3]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[4]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[5]  Yann Chevaleyre,et al.  Solving multiple-instance and multiple-part learning problems with decision trees and decision rules . Application to the mutagenesis problem , 2000 .

[6]  Zhi-Hua Zhou,et al.  Multi-Instance Learning Based Web Mining , 2005, Applied Intelligence.

[7]  Yu-Mei Chai,et al.  A Multi-Instance Learning Algorithm Based on Normalized Radial Basis Function Network , 2007, ISNN.

[8]  Balaji Padmanabhan,et al.  Evaluation of Online Personalization Systems: A Survey of Evaluation Schemes and a Knowledge-Based Approach , 2005 .

[9]  Asoke K. Nandi,et al.  Fault detection using genetic programming , 2005 .

[10]  Lars Schmidt-Thieme,et al.  Guest Editors' Introduction: Recommender Systems , 2007, IEEE Intell. Syst..

[11]  Hsin-Chia Fu,et al.  An EM based multiple instance learning method for image classification , 2008, Expert Syst. Appl..

[12]  Thomas Gärtner,et al.  Multi-Instance Kernels , 2002, ICML.

[13]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[14]  Jan Ramon,et al.  Multi instance neural networks , 2000, ICML 2000.

[15]  Yixin Chen,et al.  Image Categorization by Learning and Reasoning with Regions , 2004, J. Mach. Learn. Res..

[16]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[17]  Philip M. Long,et al.  PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples , 1996, COLT '96.

[18]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[19]  Yixin Chen,et al.  MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[21]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[22]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[23]  Giancarlo Ruffo,et al.  Learning single and multiple instance decision tree for computer security applications , 2000 .

[24]  Andreas Geyer-Schulz,et al.  Fuzzy Rule-Based Expert Systems and Genetic Machine Learning , 1996 .

[25]  Peter Auer,et al.  On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach , 1997, ICML.

[26]  César Hervás-Martínez,et al.  JCLEC: a Java framework for evolutionary computation , 2007, Soft Comput..

[27]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[28]  Edward W. Wild,et al.  Multiple Instance Classification via Successive Linear Programming , 2008 .

[29]  Zhi-Hua Zhou,et al.  Improve Multi-Instance Neural Networks through Feature Selection , 2004, Neural Processing Letters.

[30]  Zhi-Hua Zhou,et al.  Adapting RBF Neural Networks to Multi-Instance Learning , 2006, Neural Processing Letters.

[31]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[32]  Peter A. Whigham,et al.  Grammatical bias for evolutionary learning , 1996 .

[33]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[34]  Tao Mei,et al.  MILC2: A Multi-Layer Multi-Instance Learning Approach to Video Concept Detection , 2008, MMM.

[35]  Zhi-Hua Zhou,et al.  Solving multi-instance problems with classifier ensemble based on constructive clustering , 2007, Knowledge and Information Systems.

[36]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[37]  Qi Zhang,et al.  EM-DD: An Improved Multiple-Instance Learning Technique , 2001, NIPS.

[38]  Adam Tauman Kalai,et al.  A Note on Learning from Multiple-Instance Examples , 2004, Machine Learning.