@inproceedings{0d4cd533ee404b87b08c7644b37be0c9,
title = "Profile Inference from Heterogeneous Data: Fundamentals and New Trends",
abstract = "One of the essential steps in most business is to understand customers{\textquoteright} preferences. In a data-centric era, profile inference is more and more relaying on mining increasingly accumulated and usually anonymous (protected) data. Personalized profile (preferences) of an anonymous user can even be recovered by some data technologies. The aim of the paper is to review some commonly used information retrieval techniques in recommendation systems and introduce new trends in heterogeneous information network based and knowledge graph based approaches. Then business developers can get some insights on what kind of data to collect as well as how to store and manage them so that better decisions can be made after analyzing the data and extracting the needed information.",
keywords = "Heterogeneous data, Information network, Recommendation systems, Similarity, User profile",
author = "Xin Lu and Shengxin Zhu and Qiang Niu and Zhiyi Chen",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Business Information Systems, BIS 2019 ; Conference date: 26-06-2019 Through 28-06-2019",
year = "2019",
doi = "10.1007/978-3-030-20485-3_10",
language = "English",
isbn = "9783030204846",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "122--136",
editor = "Witold Abramowicz and Rafael Corchuelo",
booktitle = "Business Information Systems - 22nd International Conference, BIS 2019, Proceedings",
}