中文摘要：

协同过滤是推荐系统中应用最为广泛的方法.提出一类基于二部图一维投影与排序相结合的协同过滤算法,文中采用结构相似进行二部图投影并利用随机游走对节点排序.该方法不仅可以防止冷启动,具有较高准确度,且可扩展性良好.另外,该算法可以避免低覆盖率造成的推荐不准确.算法可以有两类不同的实现,分别是基于项协同过滤的项排序算法和基于用户协同过滤的用户排序算法,在标准数据集MovieLens上的测试表明了算法的有效性.

英文摘要：

Collaborative Filtering is the most widely used approach in recommender systems. This paper proposes a novel collaborative filtering approach through combining bipartite graph projection and ranking. In our approach bipartite graph is projected based on structural similarity and random walk is used to rank the nodes. This method can not only deal with ＂cold start problem＂ to get high precision but also have good scalability. Moreover,our method can generate the rank based on the similarity between all user-item pairs,thus making it avoids inaccuracy caused by low coverage rate. The algorithm is tested in both the item-collaborative-based item ranking way and the user-collaborative-based user ranking way. The experimental results obtained on a benchmark dataset Movielens clearly show the effectiveness of our proposed approach.