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Collaborative Filter (CF) methods supply favorably personalized predictions relying on adequate data from users. But the ratings, of new users or about new items are not always available and CF can΄t make a precise recommendation in this case. In our paper, we present our consideration on alleviating cold-start problem by using users΄ implicit feedback data, which is not the same as the traditional methods which focus completely on the sparse data. To exploit the significance of users΄ implicit feedback for alleviating cold-start problem, we present two independent strategies–the neural network-based M1 method and the collaboration-based M2 method, by which the significance of users΄ implicit feedback for cold-start recommendation has been preliminarily demonstrated. [ABSTRACT FROM AUTHOR]