Disclosed is a recommendation method and device. The recommendation method and device includes: acquiring
current user behavior data in a currently operating network
system in real time to obtain real-time user behavior data of users on the network, the
current user behavior data representing currently occurring user rating activities of the users on the network; updating existing rating data of the users, the existing rating data representing user ratings of network objects made by the users during prior interactions with the network
system or other network systems, the updating comprising modifying the user ratings in accordance with the real-time acquired user behavior data to obtain
current user rating data of the users currently interacting with the network
system; determining based upon the current user rating data, one or more similarities chosen from a group of similarities consisting of similarities between the users on the network, similarities between the network objects rated by the users, and between similarities between the users and the network objects; and sending to users recommendations for new recommended network objects to users according to one or more of the determined similarities, the recommendations being sent to users while the users are currently interacting with the network system. The present disclosure can increase recommendation precision and improve recommendation effect.