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Conventional collaborative-filtering methods use only one information source to provide recommendations. Using two sources - similar users and expert users - enables more effective, more adaptive recommendations. Conventional CF methods suffer from a few fundamental limitations such as the cold-start problem, data sparsity problem, and recommender reliability problem. Thus, they have trouble dealing with high-involvement, knowledge-intensive domains such as e-learning video on demand. To overcome these problems, researchers have proposed recommendation techniques such as a hybrid approach combining CF with content-based filtering. Because e-commerce Web sites for e-learning often have various product categories, extracting the many attributes of these categories for content-based filtering is extremely burdensome. So, it might be practical to overcome these limitations by improving the CF method itself.