Neural Personalized Topic Modeling for Mining User Preferences on Social Media

Author:

Liu Luyang1ORCID,Lin Qunyang1ORCID,Tong Haonan1ORCID,Zhu Hongyin1ORCID,Liu Ke1ORCID,Wang Min1ORCID,Zhang Chuang1ORCID

Affiliation:

1. Inspur Electronic Information Industry Co., Ltd., Beijing, China

Funder

The National Key R&D Program of China

The Shandong Key R&D Program

The Technology Program of Guangzhou, China

Publisher

ACM

Reference38 articles.

1. Cross-lingual Contextualized Topic Models with Zero-shot Learning

2. Variational Inference: A Review for Statisticians

3. David M. Blei and John D. Lafferty. 2005. Correlated Topic Models. In Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems , NIPS 2005 , December 5 --8 , 2005 , Vancouver, British Columbia, Canada]. 147--154. http://papers.nips.cc/paper/2906-correlated-topic-models David M. Blei and John D. Lafferty. 2005. Correlated Topic Models. In Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5--8, 2005, Vancouver, British Columbia, Canada]. 147--154. http://papers.nips.cc/paper/2906-correlated-topic-models

4. David M. Blei , Andrew Y. Ng , and Michael I. Jordan . 2001. Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic , NIPS 2001 , December 3 --8 , 2001 , Vancouver, British Columbia, Canada], Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani (Eds.). MIT Press, 601--608. http://papers.nips.cc/paper/2070-latent-dirichlet-allocation David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2001. Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3--8, 2001, Vancouver, British Columbia, Canada], Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani (Eds.). MIT Press, 601--608. http://papers.nips.cc/paper/2070-latent-dirichlet-allocation

5. Gaussian LDA for Topic Models with Word Embeddings

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