考虑商品重复购买周期的协同过滤推荐方法改进

Improvement of collaborative filtering recommendation considering repeated purchase cycle of commodities

传统基于用户的协同过滤商品推荐方法通常未考虑目标用户是否购买过类似商品以及商品的重复购买周期等因素,由此造成有些商品推荐的时机不对,不仅占用了推荐资源,还可能给消费者带来困扰,为此提出一种基于商品重复购买周期的改进协同过滤推荐方法。在传统协同过滤算法的基础上引入已购商品回购状态变量,根据目标用户的历史购买数据和商品重复购买周期对所购买商品的回购状态进行计算,进而得出不处于回购周期内的已购商品类集,据此对原始推荐结果进行过滤。实验结果表明,改进后的协同过滤推荐系统能有效预测顾客的购买行为,明显提高商品推荐的准确性。

Traditional user-based collaborative filtering method for commodity recommendation doesn＇t consider whether the target user has ever bought similar goods and their repeated purchase cycles.It may lead to the wrong time of commodity recommendation,which not only wastes the recommending resources but also causes the consumers a lot of trouble.So this paper proposes an improved collaborative filtering recommendation method based on commodity repeated purchase cycle.Firstly,a repurchase status variable for commodity is introduced into the traditional collaborative filtering algorithm.Then repurchase status of the purchased goods is determined according to the target user＇s shopping record and repeated purchase cycle of goods.Finally,the purchased commodity set not in the repurchase cycle is obtained and applied to filter the recommendation results by the original collaborative filtering algorithm.The experimental results show that the improved collaborative filtering recommendation system can effectively predict the shopping behavior of customers and obviously improve the accuracy of commodity recommendation.