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首页> 外文期刊>Expert Systems with Application >Extended topology based recommendation system for unidirectional social networks
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Extended topology based recommendation system for unidirectional social networks

机译：基于扩展拓扑的单向社交网络推荐系统

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摘要

The power and importance of social networks increases day by day and many social networks such as "Facebook, Twitter, Weibo and others" have more than millions of users who communicate with each other. This opportunity is triggering researchers to do studies on the social network area and supports them to do improvements for recommendation systems (RS). In this study, we propose an extension to the topology based and Friends of Friends (FoF) recommendation systems by taking into account the user actions. The proposed approach (PA) firstly classifying the data has been set into four classes and secondly an equation was computed by using the relationship of users. Our model utilizes not only the relationship of the users but also many actions and many mentions of the users to generate the recommendation to users. We evaluate the performance over precision-recall graphs and receiver operating characteristic (ROC) curves. PA extended topology based and FoF algorithm results compared with the other alternative RSs. The benchmarking study show that recommendations of extending topology based RS performs better than the extended FoF and other well-known algorithms such as graph-based and Conceptual Fuzzy Set based algorithms.
机译：社交网络的功能和重要性每天都在增加，许多社交网络（例如“ Facebook，Twitter，微博等”）拥有数以百万计的彼此通信的用户。这一机会促使研究人员在社交网络领域进行研究，并支持他们对推荐系统（RS）进行改进。在这项研究中，我们通过考虑用户的操作，提出了对基于拓扑和朋友之友（FoF）推荐系统的扩展。提出的方法（PA）首先将数据分类为四个类别，其次通过使用用户关系来计算方程。我们的模型不仅利用用户的关系，还利用用户的许多动作和提及来生成对用户的推荐。我们通过精确调用图和接收器工作特性（ROC）曲线评估性能。与其他替代RS相比，基于PA扩展拓扑和FoF算法的结果。基准测试研究表明，基于扩展拓扑的RS的建议要比扩展FoF和其他众所周知的算法（例如基于图和基于概念模糊集的算法）的性能更好。

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