HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems
HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems
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DOI：
10.1016/j.eswa.2014.05.001
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发表时间：
2014-11
期刊：
影响因子：
--
通讯作者：
Le Hoang Son
中科院分区：
文献类型：
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作者：
Le Hoang Son
推荐系统由于其在各个交叉学科领域的应用而引起了研究者的极大兴趣。模糊推荐系统（FRS）是RS的一种扩展，它基于用户的人口统计数据来计算模糊相似度，而不是硬的基于用户的程度。基于FRS研究没有给出FRS的数学定义及其代数运算和性质，模糊相似度不足以准确表达用户之间的相似性，本文将给出FRS的一个系统的数学定义，包括代数运算和性质的理论分析，并提出一种新的混合用户-的模糊协同过滤方法，该方法将基于人口统计数据的用户间模糊相似度与根据用户评分历史计算出的硬用户相似度综合到最终的相似度中，以获得较高的预测精度。在一些基准数据集上的实验结果表明，该方法获得了更好的准确率比其他相关方法。最后，以足球比赛结果预测为例说明了该方法的应用。
Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users' demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method.