@inproceedings{2e937a46ea3f4030b198990d4c77e439,
title = "User profiling by combining topic modeling and pointwise mutual information (TM-PMI)",
abstract = "User profiling is one of the key issues in personalized recommendation systems. A content curation social network is a content-centric network; it encourages users to repin items from other users and other websites. It further permits users to arrange the pins according to their interests. It is therefore possible to estimate user interest from the pins. In this paper, we propose a user profiling approach to combining topic model and pointwise mutual information (TM-PMI). We first extract a pin{\textquoteright}s description, and then apply latent Dirichlet allocation (LDA, one of the topic modeling schemes). A three-layer hierarchical Bayesian model of user-topic-word is thus obtained. Then, a personal model is obtained by selecting a set of correlated words with constraints of word probability and PMI. The experimental results confirm the efficiency of the proposed approach.",
keywords = "Latent dirichlet allocation, Pointwise mutual information, Topic modeling, User profile",
author = "Lifang Wu and Dan Wang and Cheng Guo and Jianan Zhang and Chen, \{Chang Wen\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 22nd International Conference on MultiMedia Modeling, MMM 2016 ; Conference date: 04-01-2016 Through 06-01-2016",
year = "2016",
doi = "10.1007/978-3-319-27674-8\_14",
language = "English",
isbn = "9783319276731",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "152--161",
editor = "Richang Hong and Nicu Sebe and Qi Tian and Guo-Jun Qi and Benoit Huet and Xueliang Liu",
booktitle = "MultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings",
address = "Germany",
}