Effects of User Tastes on Personalized Recommendation

Liu Jian-guo
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引用次数: 32

Abstract

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.
用户品味对个性化推荐的影响
基于用户-对象二部网络的加权投影,研究了用户品味对基于质量扩散的个性化推荐算法的影响,其中用户的品味或兴趣由他收集的对象的平均程度来定义。假设物体上的初始推荐功率应由物体的程度和用户的口味共同决定。通过引入可调参数,研究了用户口味对初始推荐功率分配配置的影响。数值结果表明,本文提出的算法可以提高排序的准确率,更重要的是提高了排序的平均分数。当数据稀疏时,算法对与用户口味度接近的对象赋予更大的推荐权,而当数据密集时,算法对与用户口味度明显不同的对象赋予更大的推荐权。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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