Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network-Reference-Cited by-同舟云学术

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Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network

Published:2019-09-05 Issue: Volume: Page:121-143
ISSN:2522-848X
Container-title:Unsupervised and Semi-Supervised Learning
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Author:

Pornwattanavichai Arisara,sakolnagara Prawpan Brahmasakha na,Jirachanchaisiri Pongsakorn,Kitsupapaisan Janekhwan,Maneeroj Saranya

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-22475-2_7

Reference26 articles.

1. Guo, G. (2012). Resolving data sparsity and cold start in recommender systems. Paper presented at the proceedings of the 20th international conference on user modeling, adaptation, and personalization, Montreal.

2. Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178, 37–51.

3. Na, L., Ying, L., Xiao-Jun, T., Hai-Wen, W., Peng, X., & Ming-Xia, L. (2016). Improved collaborative filtering algorithm using topic model. In 17th international conference on parallel and distributed computing, applications and technologies (PDCAT), 16–18 Dec 2016 (pp. 342–345).

4. Jiantao, Z., & Ning, S. (2014). User interest prediction in microblog using recommendation method. In IEEE 7th joint international information technology and artificial intelligence conference (pp. 367–370).

5. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734–749.

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