用户偏好对个性化推荐的影响
Effect of user tastes on personalized recommendation
摘要：
在本文中，基于用户-对象二分网络的加权投影，我们研究了用户的口味对基于质量扩散的个性化推荐算法的影响，其中用户的口味或兴趣是由他所收集的对象的平均度来定义的。我们认为，位于对象上的初始推荐力应该由它们的程度和用户的口味决定。通过引入一个可调参数，研究了用户偏好对初始推荐功率分布配置的影响。数值实验结果表明，该算法可以提高推荐的准确率，以平均排序得分来衡量，更重要的是，我们发现当数据稀疏时，该算法应该给那些程度接近用户口味的对象更多的推荐权，而当数据密集时，该算法应该给那些程度与用户口味明显不同的对象更多的推荐权.
英文摘要：
In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue 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 indicate that the presented algorithm could improve the accuracy, measured by the average ranking score, more importantly, we find that 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.
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。
