Mining User Interests for Personalized Tweet Recommendation on Map-Reduce Framework

Guanyao Du, Jianying Sun, Xinglong Huang, Jianjun Yu

Abstract

The tremendous growth of micro-blogging systems in recent years poses some key challenges for recommender systems, such as how to process tweet big data under distributed environment, how to striking a balance between high accurate recommendations and efficiency, and how to produce diverse recommendations for millions of users. In our opinion, accurately, instantly, and completely capturing user preferences over time is the key point for personalized tweet recommendation. Therefore, we introduce three features to model personal user interests and its evolution for tweet recommendation, including textual information, user behaviors, and time. We then offer two enhanced recommendation models: Topic-STG (Session-based Temporal Graph) model and SVD (Singular Value Decomposition) model, combining these features to learn user preference and recommend personalized tweet. To further improve the algorithm efficiency for micro-blogging big data, we provide the parallel algorithm implementation for Topic-STG and SVD models based on Hadoop Map-Reduce framework. Experiments on a large scale of micro-blogging dataset illustrate the effectiveness of the proposed models and algorithms.

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Paper Citation


in Harvard Style

Du G., Sun J., Huang X. and Yu J. (2017). Mining User Interests for Personalized Tweet Recommendation on Map-Reduce Framework . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 201-208. DOI: 10.5220/0006274102010208


in Bibtex Style

@conference{iceis17,
author={Guanyao Du and Jianying Sun and Xinglong Huang and Jianjun Yu},
title={Mining User Interests for Personalized Tweet Recommendation on Map-Reduce Framework},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={201-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006274102010208},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Mining User Interests for Personalized Tweet Recommendation on Map-Reduce Framework
SN - 978-989-758-247-9
AU - Du G.
AU - Sun J.
AU - Huang X.
AU - Yu J.
PY - 2017
SP - 201
EP - 208
DO - 10.5220/0006274102010208