Abstract: Recommendations from Collaborative Filtering (CF) recommender algorithms have low timeliness. To solve the problem, an information aging-based collaborative filtering algorithm is proposed by combining information age method. The algorithm builds a model to evaluate the timeliness of items based on users hit records to predict the probabilities of the items being clicked at the present time. To consider comprehensively users interests and the timeliness of items, the model and item-based collaborative filtering recommender algorithm are combined to find the nearest neighbor collection. Experimental results show that comparing with traditional collaborative filtering recommender algorithm the proposed algorithm can improve the timeliness of recommendations.