基于改进Canopy聚类的协同过滤推荐算法

推荐系统通过建立用户和信息产品之间的二元关系，利用用户行为产生的数据挖掘每个用户感兴趣的对象并进行推荐，基于用户的协同过滤是近年来的主流办法，但存在一定局限性：推荐时需要考虑全部用户，而单个用户往往只与少部分用户类似。为了解决这个问题，提出了基于改进Canopy聚类的协同过滤推荐算法，将用户模型数据密度、距离与用户活跃度结合，计算用户数据权值，对用户模型数据进行聚类。由于结合了Canopy的聚类思想，同一用户可以属于不同的类，符合用户可能对多领域感兴趣的情况。最后对每个Canopy中的用户进行相应的推荐，根据聚类结果与用户评分预测用户可能感兴趣的对象。通过在数据集MovieLens和million songs上与对比算法进行MAE、RMSE、NDGG三个指标的比较，验证了该算法能显著提高推荐系统预测与推荐的准确度。

Collaborative filtering recommendation algorithm based on improved Canopy clustering

By establishing the binary relationship between users and information products, the recommender system makes use of the data generated by user behavior to mine the objects that each user is interested in and make recommendations, user-based collaborative filtering has been a mainstream approach in recent years, but it has a limitation: recommendations need to consider all users, and a specific user is often similar to a small number of users. To solve this problem, this paper proposed a collaborative filtering algorithm based on improved Canopy clustering, which combined the user model data density, distance and user activity to calculate the weights of the user, then clustered the user model data, the idea of clustering based on Canopy made one user could belong to different classes, which fit in with situations that users might be interested in multiple areas. Finally, corresponding recommendations were made for each user in Canopy, and it predicted the objects that users might be interested in based on the clustering result and user score. By comparing with other algorithms on two real-world data sets MovieLens and million songs, it verifies that the proposed algorithm can significantly improve the accuracy of the recommender system.