Abstract: The traditional collaborative filtering(CF) recommendation is usually based on the assumption that the data is static.When the data is sparse,it usually leads to low recommendation accuracy.With this in mind,some studies try to add supplemen-tary information such as changes in user interest and the trustiness of recommendation ability to their strategies.However,most of them lack of consideration for the abnormal situations that mislead or interfere with the recommendation,such as malicious changes in user interests and fluctuations in the recommendation ability,and are difficult to ensure the anti-attack,recommendation stability and reliability of the recommendation system.By introducing interest time-effective similarity and re-evaluation on re-commendation trust degree,this paper proposes a time-effective nearest neighbor trusted selection strategy based collaborative filtering recommendation method.It takes into account two key factors,that is,the abnormal change of user interest and the fluctuation of user recommendation ability,which affect the quality of target user’s neighbor filtering,and construct a recommendation process that includes three strategies,that is,time-effective nearest neighbor selection,trusted nearest neighbor selection and ra-ting prediction.Based on MovieLens dataset and Amazon video game dataset,and with the metrics such as mean absolute error(MAE),average prediction shift(APS),and attacker identification of precision ratio,recall ratio and F1 means,the performance of the proposed strategy and other six baselines are compared.The results show that our strategy outperform the baselines in re-commendation accuracy,anti-attack and attacker identification.

Key words: Collaborative filtering, Time-effective nearest neighbor, Trusted nearest neighbor, Recommendation accuracy, Anti-attack, Attacker recognition