The invention discloses an individualized recommending method and system based on an element path. The recommending method comprises the following steps: building a heterogeneous network graph through collecting article content information, social relationship among users and user historical behavior information; utilizing a relevance measurement method based on heterogeneous bi-directional random walk to measure the preference degrees of a user on articles based on semantics; utilizing machine learning to synthesize the preference degrees of the user under different semantics and predict the preference degrees of the user on unselected articles so as to recommend favorable articles to the user. According to the system, the accuracy, novelty and diversity of the recommending effect are effectively improved through comprehensively utilizing the additive information of other article types to a target article type based on the content, cooperation filtering and the social network relationship and digging the semantic features of the information.