二分网络的度相关性对个性化推荐的影响
Degree correlation effect of bipartite network on personalized recommendation
摘要：
本文通过引入一种新的基于扩散过程的用户相似度指标，提出了一种改进的协同过滤（MCF）算法，该算法比标准的协同过滤算法具有更高的准确性。在该算法中，用户和对象之间的程度相关性被考虑和嵌入到相似性指数的可调参数。在基准数据集上的数值模拟表明，在最优情况下，MCF的算法精度（以平均排名得分衡量）进一步提高了18.19%。此外，算法性能的两个重要标准，多样性和流行性，也被考虑在内。数值实验结果表明，该算法可以提供更多的多样性和较少的流行，例如，当推荐列表包含10个对象，多样性，衡量汉明距离，提高了21.90%。
英文摘要：
In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
