Aiming at the problems of data sparsity and cold start in the current recommendation system, a collaborative filtering method fusing Overlapping Community Regularization and Implicit Feedback (OCRIF) was proposed, which not only considers the community structure of users in the social network, but also integrates the implicit feedback of user rating information and social information into the recommendation model. In addition, as network representation learning can effectively learn the nodes? neighbor information on global structure of social network, a network representation learning enhanced OCRIF (OCRIF+) was proposed, which combines the low dimensional representation of users in social network with user commodity features, and can represent the similarity between the users and the membership degrees of the users to the interest communities more effectively. Experimental results on multiple real datasets show that the proposed method is superior to the similar methods on the recommendation effect. Compared with TrustSVD (Trust Support Vector Machine) method, the proposed method has the Root Mean Square Error (RMSE) decreased by 2.74%, 2.55% and 1.83% respectively, and Mean Absolute Error (MAE) decreased by 3.47%, 2.97% and 2.40% respectively on FilmTrust, DouBan and Ciao datasets.