基于异构初始资源配置的改进HeatS + ProbS混合推荐算法
An improved HeatS+ProbS hybrid recommendation algorithm based on heterogeneous initial resource configurations
摘要：
基于网络的用户-对象链接预测推荐算法近年来取得了显著的发展。对于二分图，这种算法中的资源重新分配类似于热扩散（HeatS）或概率扩散（ProbS）过程。最好的算法是一个混合的HeatS和ProbS技术与同质的初始资源配置，同时满足高精度和大的多样性。我们调查的HeatS+ProbS混合算法的初始配置的异质性的影响，并发现，在这个新的设置，可以进一步提高推荐的准确性和多样性。数值实验表明，改进是鲁棒的。
英文摘要：
Network-based recommendation algorithms for user-object link predictions have achieved significant developments in recent years. For bipartite graphs, the reallocation of resource in such algorithms is analogous to heat spreading (HeatS) or probability spreading (ProbS) processes. The best algorithm to date is a hybrid of the HeatS and ProbS techniques with homogenous initial resource configurations, which fulfills simultaneously high accuracy and large diversity. We investigate the effect of heterogeneity in initial configurations on the HeatS+ProbS hybrid algorithm and find that both recommendation accuracy and diversity can be further improved in this new setting. Numerical experiments show that the improvement is robust.
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。
