us - lda:基于单向社交网络中用户的社交关系和兴趣的用户推荐

Ke Xu, Y. Cai, Huaqing Min, Xushen Zheng, Haoran Xie, Tak-Lam Wong
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引用次数: 8

摘要

人口的快速增长给人们在单向社交网络中寻找新的关注者提出了挑战。从直觉上看,用户将他人作为关注者的动机可能是出于兴趣和社交关系。因此,为了获得更好的建议,同时考虑这两个因素是值得的。之前的推荐人对隐式关注的工作是否反馈不合格,主要是因为用户的偏好推断比较粗糙,无法区分一个人是基于社会关系还是个人兴趣来关注另一个人。在本文中,我们提出了一种新的用户推荐方法,该方法能够推荐与目标用户有相似兴趣和更紧密社会联系的候选关注者。以us - lda为核心,设计了一种新颖的话题模型,针对潜在兴趣话题集和社会话题集对用户的偏好进行联合建模。使用Twitter数据集进行的实验证明,本文提出的方法在提高准确率、转化率F1分数和NDCG方面是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UIS-LDA: a user recommendation based on social connections and interests of users in uni-directional social networks
The rapid growth of population has posed a challenge to people for discovering new followees in uni-directional social networks. Intuitively, a user's adoption of others as followees may motivated by her interest as well as social connection. Therefore, it is worth-while to consider both factors at the same time for better recommendations. Previous recommender works on implicit follow or not feedbacks become unqualified, mainly because of the coarse users' preferences inferring, which cannot distinguish whether one follows the other is based on her social connection or individual interest. In this paper, we present a new user recommendation method which is capable of recommending candidate followees who have similar interest and closer social connection relevant to a target user. As its core, a novel topic model namely UIS-LDA is designed to jointly model a user's preferences with respect to the set of latent interest topics and social topics. The experiments using Twitter dataset proves that our proposed method effective in improving the Precision, Conversion Rate F1 score and NDCG.
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