Information systems, such as information retrieval machines and recommendation systems, utilize various user information and history behaviors to provide personalized content to users. However, a debate on whether the personalization in information systems can trigger the online echo chamber effect has emerged. The online echo chamber effect describes the situation that Internet users are segregated into groups based on common interests or opinions and their existing views or confirmation bias are reinforced by repetition. Based on the idea that the strong community structure of the user network suggests the emergence of the echo chamber effect, we propose a brand new methodology based on temporal community evolution to detect the echo chamber effect in recommender systems. A two-layer temporal user network is constructed, with the first layer representing the user taste similarity and the second layer encoding potential information flows between users. Then, we apply an estrangement confinement based algorithm to detect the temporal communities in the two-layer temporal network. Our experiment results on the MovieLens dataset suggest the emergence of the echo chamber effect. Moreover, we find that the echo chamber effect is becoming more remarkable over time. In addition, we observe that some users tend to stay in one community over time. These users are potentially affected by the echo chamber effect and have a higher mean node strength in both network layers.