基于观点传播的改进相似性计算评分预测方法

通过研究网络结构上的观点传播与协同过滤算法,基于对观点传播算法的优化,提出了基于用户相似和物品相似推荐系统评分预测算法.设计的算法修正了现有相似研究中在目标比较相似时,相似性结果为零的问题,将用户(或物品)的相似度定义为用户(或物品)间的观点数目和差异在相应复杂网络中的传播结果,并提出了相应的推荐算法.在MovieLens数据集上的实验结果证明,提出的算法与几种典型的现有方法相比较,具有更高的准确性,并且优于观点传播算法.

Online-Rating Prediction Based on Opinion Spreading and an Improved Similarity Calculation Method

The relation between users and items in the recommendation system was considered as a complex network structure, and the binary relation between users and items was used to construct a graph model,based on which a diffusion dynamics method was introduced to study the recommendation algorithm.Through studying the ideas spreading in the network structure and collaborative filtering algorithms,two optimized algorithms were presented based on user similarity and item similarity,respectively.The proposed approach provides a correction method for zero value problems of similarity calculation,ignored in most existing publications.The similarity of users (or items) was defined as the number of corresponding views which a user (or items) owns and differences between those viewpoints spreading in the complex network.Using MovieLens data set,the experiments show that the presented algorithm has better performance than the collaborative filtering algorithm based on Pearson correlation coefficient and some other existing methods.