【摘要】 为解决复杂的网络信息无法对用户进行精准推荐的情况,改进传统协同过滤算法,将混沌粒子群算法与协同过滤算法融合使用。在传统粒子群算法中加入混沌扰动并随着迭代调整惯性权重,对用户进行聚类优化。获取目标用户之后,通过判断目标用户属于哪个聚类,在该聚类内部进行协同过滤计算。通过与其它算法之间的对比实验,验证了基于混沌粒子群聚类优化的协同过滤推荐算法相较其它算法具有更低的平均绝对误差和更高的准确率。更多还原

【Abstract】 To solve the situation that complex network information cannot accurately recommend users,the traditional collaborative filtering algorithm was improved,and the chaotic particle swarm optimization algorithm and the collaborative filtering algorithm were used together.In the traditional particle swarm optimization algorithm,chaotic disturbances were added and the inertia weight was adjusted with iteration to optimize the user clustering.After acquiring the target user,the cluster to which the target user belongs was determined,and collaborative filtering calculation was performed within the cluster.Through comparison experiments with other algorithms,it is verified that the collaborative filtering recommendation algorithm based on chaotic particle swarm optimization has lower average absolute error and higher accuracy than other algorithms.更多还原