With the prosperity of e-commerce, the recommendation service rises rapidly due to its significant performance on promotion of consumer satisfaction level and sales. On one hand, it helps the consumer to find the suitable products in a much easier way. On the other, it explores the potential need of the consumer therefore steps up the deal. In order to make improvement on user stickiness, the recommendation system is now recognized as a vital role in intensive competition. However, it has an obvious weakness when helping consumers choose high-tech products where it requires much strict technical knowledge and it is not effective to recommend new products due to the lack of ratings. This research provides a new recommendation method related to high-tech products based on consumer's preference with less complexity and more effectiveness. It aims to make the new products equivalent to the old ones during the computing process therefore improve the effectiveness of new product recommendation.
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