基于中间商品兴趣度加权的内容推荐算法

Content Recommendation Algorithm Based on Intermediate Goods Interest Weighting

内容推荐算法是在电子商务网站中应用比较广泛的推荐技术之一,主要针对商品之间属性相似度和用户对商品兴趣度建模.但传统的内容推荐算法不能及时反映用户对商品兴趣度的时间变化,基于这点提出一种基于中间商品兴趣度加权的内容推荐算法.改进算法增加了兴趣度时间权重函数和商品时间权重函数,可以提高商品推荐准确率,仿真试验结果证明该算法是有效的.

The content recommendation algorithm is one of the most widely used recommendation technologies in e-commerce,mainly on the attribute similarity between commodities and users’interests in the past commodity interest modeling.However,the user’s interest in goods can not be reflected in time,aiming at this shortcoming,a content recommendation algorithm based on intermediate goods interest weighting is proposed.Improved algorithm can increase interest time weight function.The algorithm can improve the quality of goods,the simulation results show the effectiveness of the method.