A Social Interaction Activity Based Time-Varying User Vectorization Method for Online Social Networks
Abstract
In this paper, we consider the problem of user modeling in online social networks, and propose a social interaction activity based user vectorization framework, called the time-varying user vectorization (Tuv), to infer and make use of important user features. Tuv is designed based on a novel combination of word2vec, negative sampling and a smoothing technique for model training. It jointly handles multi-format user data and computes user representing vectors, by taking into consideration user feature variation, self-similarity and pairwise interactions among users. The framework enables us to extract hidden user properties and to produce user vectors. We conduct extensive experiments based on a real-world dataset, which show that Tuv significantly outperforms several state-of-the-art user vectorization methods.
Cite
Text
Hao and Huang. "A Social Interaction Activity Based Time-Varying User Vectorization Method for Online Social Networks." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/527Markdown
[Hao and Huang. "A Social Interaction Activity Based Time-Varying User Vectorization Method for Online Social Networks." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/hao2018ijcai-social/) doi:10.24963/IJCAI.2018/527BibTeX
@inproceedings{hao2018ijcai-social,
title = {{A Social Interaction Activity Based Time-Varying User Vectorization Method for Online Social Networks}},
author = {Hao, Tianyi and Huang, Longbo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {3790-3796},
doi = {10.24963/IJCAI.2018/527},
url = {https://mlanthology.org/ijcai/2018/hao2018ijcai-social/}
}