Optimization of LDA text microblogging recommendation algorithm based learning to rank

DOI

10.2991/ameii-16.2016.22How to use a DOI?

Keywords

Personalized recommendation, LDA, Short text, Microblogging, TF-IDF

Abstract

With the recent rise of web3.0 hot, personalized recommendation social networks become an important aspect of research. Social networks on behalf of the domestic microblogging abnormal hot, more and more domestic and foreign research applied over microblogging. Because the characteristics of micro-Bo short text, LDA topic model is more applicable to micro-blog user's interest analysis. Firstly, the use of the network topology, 10 to find a candidate set target users interested users, and then by LDA microblogging users of potential interest analysis to get a point of interest microblogging users, which are interested in the candidate set recommended target users . Experiments show that the method based on TF-IDF with respect to micro-blog content for the word to get user interest method more effective and efficient.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yuxiang Xu
PY  - 2016/04
DA  - 2016/04
TI  - Optimization of LDA text microblogging recommendation algorithm based learning to rank
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 109
EP  - 113
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.22
DO  - 10.2991/ameii-16.2016.22
ID  - Xu2016/04
ER  -