Overcoming small-size training set problem in content-based recommendation
Yen‐Hsien Lee, Tsang-Hsiang Cheng, Ci-Wei Lan, Chih‐Ping Wei, Paul Jen‐Hwa Hu · 2009
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.