As a widely used recommendation algorithm in the industry, collaborative filtering algorithm can predict the likely favorite items based on the user's historical behavior records. However, the traditional collaborative filtering algorithms do not take into account the drifting of user interests, and there are also some deficiencies when the recommendation's timeliness is considered. To solve these problems, the measure method of similarity was improved by combining with the characteristics of user interests change with time. At the same time, an enhanced time attenuation model was introduced to measure the predictive value. By combining these two ways together, the concept drifting problem of user interests was solved and the timeliness of the recommendation algorithm was also considered. In the simulation experiment, predictive scoring accuracy and Top N recommendation accuracy were compared among the proposed algorithm, UserCF, TCNCF, PTCF and TimesSVD++ algorithm in different data sets. The experimental results show that the improved algorithm can reduce the Root Mean Square Error (RMSE) of the prediction score, and it is better than all the compared algorithms on the accuracy of Top N recommendation.