Abstract: Collaborative filtering is the recommendation technology which is applied most widely in the personalized recommendation system.However，existing collaborative filtering algorithms do not consider the relationship between the every year specific event and the user purchase behavior.For this reason，this paper presents a collaborative filtering algorithm considering the year schedule event.It introduces the time weight function，makes recommendation of the commodity which purchased by the consumer of close neighbor and which’s purchased time is close to current user access time and belong to the same period to be higher.So the recommendation accuracy of collaborative filtering algorithms can be improved.

Key words: annual schedule events, collaborative filtering, personalized recommendation, time weight function

摘要： 协同过滤是个性化推荐系统中应用最广泛的推荐技术，现有的协同过滤算法不能反映出每年特定的事件与用户购买行为的关联性。针对这个问题，提出了一种考虑年度日程表事件的协同过滤算法，引入了时间权值函数，使得同一时期的越接近当前用户访问时间的近邻用户购买商品的推荐度越高，提高了协同过滤算法的推荐精度。

关键词: 日程表事件, 协同过滤, 个性化推荐, 时间权值函数