The invention discloses a
collaborative filtering recommendation method for integrating time
contextual information, which is used for integrating the time
contextual information on the basis of an original item-based
collaborative filtering recommendation
algorithm and an original user-based
collaborative filtering recommendation
algorithm and combining the original item-based collaborative filtering recommendation
algorithm and the original user-based collaborative filtering recommendation algorithm into a uniform algorithm. The collaborative filtering recommendation method comprises the steps of for the user-based collaborative filtering recommendation algorithm, firstly, integrating a time
attenuation function in a user similarity calculation stage; then, clustering items, and training interest attenuation factors of a user on an article category; finally, integrating the time
attenuation function in a rating prediction stage, wherein for the item-based collaborative filtering recommendation algorithm, the process is similar to the process of the user-based collaborative filtering recommendation algorithm, and the two algorithms can be finally combined into the uniform algorithm. According to the collaborative filtering recommendation method disclosed by the invention, the time
attenuation function is introduced in both the
similarity computation stage and the rating prediction stage, different time attenuation factors are used for different types of items by different users, and thus the prediction accuracy can be effectively increased.