A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Fab: content-based, collaborative recommendation
Communications of the ACM
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Comparing feature-based and clique-based user models for movie selection
Proceedings of the third ACM conference on Digital libraries
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Retrieval
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evaluating Feature Selection Methods for Learning in Data Mining Applications
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
IEEE Transactions on Knowledge and Data Engineering
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Effective, personalized recommendations are central to cross-selling, a common business strategy that suggests additional items (products or services) to customers for their consideration. Content-based recommendation and collaborative filtering represent two salient approaches for automated recommendations. The content-based approach uses essential features (attributes) of items to make recommendations, without making reference to the preferences of other customers. Although content-based recommendation techniques have been shown effective in various scenarios, their utilities and value depend on the availability of a large number of training examples. In this study, we propose a collaborative content-based (COCO) recommendation technique that uses a collaboration-based expansion approach to address the small-size training set problem, a common challenge faced the content-based recommendation approach. We empirically examine the effectiveness of the proposed technique for book recommendations and include a pure content-based technique as a performance benchmark. According to our evaluation results, the proposed COCO technique substantially outperforms the benchmark technique.