Zeng Wei, Zeng An, Liu Hao, Shang Ming-Sheng, Zhang Yi-Cheng
Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; State Key Laboratory of Networking and Switching Technology, Beijing, P.R. China; Department of Physics, University of Fribourg, Fribourg, Switzerland.
Department of Physics, University of Fribourg, Fribourg, Switzerland; School of Systems Science, Beijing Normal University, Beijing, P.R. China.
PLoS One. 2014 Oct 24;9(10):e111005. doi: 10.1371/journal.pone.0111005. eCollection 2014.
Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.
推荐系统旨在帮助个体用户在迅速增长的信息量中进行浏览。最成功的推荐技术之一是协同过滤，该技术已得到广泛研究并在电子商务中得到了广泛应用。此算法面临的挑战之一是如何准确量化用户对和物品对之间的相似度。在本文中，我们采用多维缩放（MDS）方法来测量用户-物品二分网络中节点之间的相似度。MDS方法可以通过消除噪声从网络中提取基本的相似度信息，从而提供网络结构的图形化展示。基于从MDS测得的相似度，我们发现基于物品的协同过滤算法可以优于基于扩散的推荐算法。此外我们还表明，该方法倾向于推荐不受欢迎的物品，并从长期来看增加网络的全局多样性。
