Traditional recommendation algorithms predict the latent interest of an active user by collecting rating informa-tion from other similar users or items.Recently,more and more recommendation systems attempt to involve social relations to improve recommendation performance.However,the existing works either leave out the user reliability or cannot capture the correlation between two users who are similar but not so-cially connected.Besides,they also take the trust value between users either 0 or 1,thus degenerating the prediction accuracy.In this paper,we propose an efficient social affect model,multi-affect(ed),for recommendation via incorporating both users\' reliability and influence propagation.Specifically,the model contains two main components,i.e.,computation of user relia-bility and influence propagation,designing of user-shared fea-ture space.Firstly,a reliability calculation strategy based on user similarity is developed for measuring the recommendation accuracy between users.Then,the factor of influence propaga-tion relationship among users is taken into consideration.Fi-nally,the multi-affect(ed) model is developed with user-shared feature space to generate the predicted ratings.Experimental results demonstrate that the proposed model achieves better accuracy than other counterparts recommenda-tion techniques.