协同过滤推荐算法在豆瓣网络数据上的研究

在豆瓣网络数据上对传统的协同过滤推荐算法进行改进，分别考虑最近邻和有向相似度方向的作用，对图书、电影和音乐收藏列表进行个性化推荐。推荐的结果在准确度、多样性和新奇性三种被广泛使用在衡量推荐算法效果的指标上进行比较和分析。结果表明，相比传统协同过滤推荐算法，两种改进算法均能够保证多样性和新奇性，同时最近邻算法可有效降低算法复杂度，而有向相似度算法则具有更高的推荐准确度。

Research of Collaborative Filtering Recommendation Algorithm on Douban Network Data

This paper improved the collaborative filtering recommendation algorithm by considering the nearest neighbor and directed similarity in Douban network data. Then, the improved algorithms were used to recommend books, movies and music for Douban users. The recommended results are carefully compared and analyzed in terms of three well-know indicators including accuracy, diversity and novelty. It is shown that the nearest neighbor algorithm has much lower computational complexity and the directed similarity algorithm obtains higher accuracy, while all these three algorithms have similar diversity and novelty of the recommended results, by comparing with the traditional collaborative filtering recommendation algorithm.