Overcoming small-size training set problem in content-based recommendation

Abstract

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.

Improving the Performance of Text Categorization Models used for the Selection of High Quality Articles

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Privacy‐preserving dish‐recommendation for food nutrition through edging computing

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