Relational learning exploits relationships among instances manifested in a network to improve the predictive performance of many network mining tasks. Due to its empirical success, it has been widely applied in myriad domains. In many cases, individuals in a network are highly idiosyncratic. They not only connect to each other with a composite of factors but also are often described by some content information of high dimensionality specific to each individual. For example in social media, as user interests are quite diverse and personal; posts by different users could differ significantly. Moreover, social content of users is often of high dimensionality which may negatively degrade the learning performance. Therefore, it would be more appealing to tailor the prediction for each individual while alleviating the issue related to the curse of dimensionality. In this paper, we study a novel problem of Personalized Relational Learning and propose a principled framework PRL to personalize the prediction for each individual in a network. Specifically, we perform personalized feature selection and employ a small subset of discriminative features customized for each individual and some common features shared by all to build a predictive model. On this account, the proposed personalized model is more human interpretable. Experiments on real-world datasets show the superiority of the proposed PRL framework over traditional relational learning methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781611974874
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017


Other17th SIAM International Conference on Data Mining, SDM 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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