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- W1963834128 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." @default.
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- W1963834128 date "2009-08-12" @default.
- W1963834128 modified "2023-10-18" @default.
- W1963834128 title "Overcoming small-size training set problem in content-based recommendation" @default.
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- W1963834128 doi "https://doi.org/10.1145/1593254.1593268" @default.
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