融合上下文信息与核密度估计的协同过滤推荐

随着互联网信息技术的迅速发展,网络数据量快速增长,如何在海量数据中找到用户感兴趣的信息并实现个性化推荐是目前重要的研究方向.协同过滤算法作为推荐系统中的经典方法被广泛应用于不同场景,但是仍然存在数据稀疏,以及在计算相似度时不能考虑到所有数据的问题,只能够利用具有共同评分的数据,严重影响了推荐的精确度.针对上述存在的问题...

Collaborative Filtering Recommendation Algorithm for Fusion Context Information and Kernel Density Estimation

With the development of Internet information technology and the growth of network data,how to find the information that users are interested in from the massive data and realize personalized recommendation is an important research direction at present.As a classic method in the recommendation system,collaborative filtering algorithm is widely used in different scenes,but it still cannot solve the problem of data sparsity,and in the calculation of similarity,it cannot take all the data into account and can only use the common data,which seriously affects the accuracy of recommendation.Aiming at the problems above,we propose a collaborative filtering recommendation fusing context information and kernel density estimation.The algorithm is based on the user and project their own context information and existing user rating data for processing,based on kernel method respectively to build user and project estimation model,fully taping the interest distribution of user and project,so as obtain more accurate similarity of user and project and reduce the prediction error.The validation on the open data set shows that compared with the traditional collaborative filtering algorithm,the proposed algorithm can effectively improve the accuracy of recommendation.