Authors:
Aristomenis S. Lampropoulos
;
Paraskevi S. Lampropoulou
and
George A. Tsihrintzis
Affiliation:
University of Piraeus, Greece
Keyword(s):
Transductive SVM, Recommender system, Ensemble of classifiers.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Based Data Mining and Complex Information Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
In this paper, we address the recommendation process as a classification problem based on content features and a bank of Transductive SVMclassifiers that capture user preferences. Specifically, we develop an ensemble of Transductive SVM(TSVM) classifiers, each of which utilizes a different feature vector extracted fromdifferent semantic meta-data such as actors, directors, writers, editors and genres. The ensemble classifier allows our system to utilize feature vectors of meta-data from a database and to make personalized recommendations to users. This is achieved through the property of TSVM classifiers to utilize a large amount of available unlabeled data together with a small amount of labeled data that constitute the rated movies of a user. The proposed method is compared to a TSVM classifier which utilizes a feature vector extracted from only ratings of users. The experimental results based on the MovieLens data set indicated that our classifier based on an ensemble of TSVM with
content meta-data yield higher accuracy recommendations when compared to the TSVM classifier that utilized only user ratings.
(More)