By recommending users with candidate objects such as movies, music, books, friends, groups, or goods, the users can obtain information about the corresponding recommended candidate objects without the need of active search, providing the users with a way to passively obtain information. Currently, one kind of method for pushing recommendation information is mainly implemented based on a social-network relationship between users. For example, if a user A watches a movie M, and the user A and a user B are in a friend relationship, the user A may recommend the movie M to the user B.
However, for the current recommendation information method, only the social-network relationship between the users is considered. But the users having the social-network relationship do not necessarily have same recommendation need. For example, a user A and a user B are in a friend relationship, but the user A and the user B may have totally different movie preferences. As a result, recommending a movie M watched by the user A to the user B is inaccurate. As can be seen, for current method for pushing recommendation information based on the social-network relationship, the recommendation result often is inaccurate, and such method may need to be improved.