用户兴趣对个性化推荐的影响

Effects of User Tastes on Personalized Recommendation

利用用户-产品二部分图中度量同类节点相似性的加权映射方法,研究了用户的兴趣对基于物质扩散的个性化推荐算法的影响并提出了相应的改进算法,其中用户的兴趣定义为用户所选择过产品的平均度。该算法中在推荐过程中假设用户收集产品被赋予的推荐强度应由用户的兴趣点和产品自身的度一起决定。数值实验结果显示该算法可以提高原物质扩散算法的准确度。进一步,当数据集稀疏的时候,算法应该赋予与用户兴趣点相近的产品更大的推荐强度;随着数据集变得越来越稠密,应该赋予用户的兴趣点外的其他产品更多的权重以提高算法的准确度和推荐列表差异性。

Based on a weighted projection of the user-object bipartite network,the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm are studied,where a user tastes or interests are defined by the average degree of the objects he has collected.It is assumed that the initial recommendation power located on the objects should be determined by both of their degree and the users tastes.By introducing a tunable parameter,the user taste effects on the configuration of initial recommendation power distribution are investigated.The numerical results show that the presented algorithm could improve the accuracy,measured by the average ranking score,more importantly.When the data is sparse,the algorithm should give more recommendation power to the objects whose degrees are close to the users tastes,while when the data becomes dense,it should assign more power on the objects whose degrees are significantly different from user's tastes.