Collaborative filtering is one of the most popular and effective techniques available today in the recommender system. However, most of them use symmetric similarity measures. Therefore, the default effect and the role of the pair of users are the same, but in practice this may not be true. In addition, they only logically demonstrate the existence of a priority relationship between two users rather than the level of the relationship in practice. In this paper, we propose a new approach for the collaborative filtering based on the variation analysis of the implication index. An asymmetric measure is developed which can be used to rank or filter information based on the variation of the implication index by a counter-example. This measure provides a meaningful recommendation with a certain level of implication. Experimental results shown that the proposed approach can overcome the drawbacks in the traditional recommender systems.