一种优化的协同过滤推荐算法

协同过滤技术被成功地应用于个性化推荐系统中．随着电子商务系统用户数目和商品数目的日益增加，整个项目空间上用户评分数据极端稀疏，传统的相似性度量方法存在一定的不足．在引入项目评分预测思想的基础上，考虑到数据稀疏性带来的影响，采用修正的条件概率方法计算项目相似性，提出一种优化的协同过滤推荐算法，计算结果更具有实际意义和准确性．实验表明，该算法能够有效避免传统方法带来的弊端，提高系统的推荐质量．

An Optimized Collaborative Filtering Recommendation Algorithm

Collaborative filtering is used extensively in personalized recommendation systems With the development of E commerce, the magnitudes of users and commodities grow rapidly, resulting in the extreme sparsity of user rating data Traditional similarity measure methods work poor in this situation Considering the extreme sparsity of user rating data, collaborative filtering algorithm based on item rating prediction is introduced, then the item similarity is computed by using a new revised conditional probability expression, the quality of the recommended result can be effectively improved Finally an optimized collaborative filtering recommendation algorithm is presented It can be proved that the new algorithm presented has a better performance corresponding to the known algorithm The experiment shows that the approach is successful