Recommendation system can greatly alleviate the information overloadin the big data era. Existing recommendation methods, however, typically focus on pre-dicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation meth-ods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED (PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis (PLSA) to extract users’ interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean dis-tance is adopted to compute the users’ simi-larity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors’ ratings. We evaluate PBUED on two datasets and experi-mental results show PBUED can lead to better predicting performance and ranking perfor-mance than other approaches.