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首页> 外文期刊>Journal of Intelligent Information Systems >Inferring social network user profiles using a partial social graph
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Inferring social network user profiles using a partial social graph

机译：使用部分社交图推断社交网络用户配置文件

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User profile inference on online social networks is a key task for targeted advertising and building recommender systems that rely on social network data. However, current algorithms for user profiling suffer from one or more of the following limitations: (1) assuming that the full social graph or a large training set of crawled data is available for training, (2) not exploiting the rich information that is available in social networks such as group memberships and likes, (3) treating numeric attributes as nominal attributes, and (4) not assessing the certainty of their predictions. In this paper, to address these limitations, we propose an algorithm named Partial Graph Profile Inference+ (PGPI+). The PGPI+ algorithm can accurately infer user profiles under the constraint of a partial social graph. PGPI+ does not require training, and it lets the user select the trade-off between the amount of information to be crawled for inferring a user profile and the accuracy the inference. Besides, PGPI+ is designed to use rich information about users when available: user profiles, friendship links, group memberships, and the "views" and "likes" from social networks such as Facebook. Moreover, to also address limitations 3 and 4, PGPI+ considers numeric attributes in addition to nominal attributes, and can evaluate the certainty of its predictions. An experimental evaluation with 31,247 user profiles from the Facebook and Pokec social networks shows that PGPI+ predicts user profiles with a higher accuracy than several start-of-the-art algorithms, and by accessing (crawling) less information from the social graph. Furthermore, an interesting result is that some profile attributes such as the status (student/professor) and genre can be predicted with more than 95 % accuracy using PGPI+.
机译：在线社交网络上的用户个人资料推断是针对性广告和构建依赖于社交网络数据的推荐系统的一项关键任务。但是，当前用于用户配置文件的算法存在以下一个或多个限制：（1）假定可以使用完整的社交图或爬网数据的大量训练集进行训练，（2）不利用可用的丰富信息在诸如团体成员身份之类的社交网络中，（3）将数字属性视为名义属性，（4）不评估其预测的确定性。在本文中，为了解决这些局限性，我们提出了一种名为部分图轮廓推断+（PGPI +）的算法。 PGPI +算法可以在部分社交图的约束下准确推断用户个人资料。 PGPI +不需要培训，它使用户可以选择要爬网以推断用户配置文件的信息量与推断准确性之间的权衡。此外，PGPI +旨在在可用时使用有关用户的丰富信息：用户个人资料，友情链接，组成员身份，以及来自社交网络（如Facebook）的“视图”和“喜欢”。此外，为了解决限制3和4，PGPI +还考虑了名义属性之外的数字属性，并且可以评估其预测的确定性。对来自Facebook和Pokec社交网络的31,247个用户个人资料进行的实验评估表明，PGPI +预测用户个人资料的准确性要高于几种最先进的算法，并且可以从社交图中访问（搜寻）较少的信息。此外，一个有趣的结果是，使用PGPI +可以预测某些轮廓属性，例如状态（学生/教授）和体裁，其准确性超过95％。

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