Skip to main content
SpringerLink
Log in

An integrated topic modeling and auto-encoder for semantic-rich network embedding and news recommendation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In recent years, network representation learning is considered as a crucial research direction which explicitly supports multiple problems in information analysis and mining (INAM) domain. Among downstream tasks of INAM, news recommendation is considered as an important task, especially in semantic-rich/heterogeneous networks. Most of previous news recommendation models are mostly relied on the collaborative filtering (CF) approach. The CF-based techniques support to analyze the historical user–item interacting relationships. These relationships are used to extract latent factors and characterize the user’s preferences which are later utilize to facilitate the different recommendation tasks. Recent attempts also focus on the integrations of news recommendation with complex representation learning techniques to leverage the accuracy performance. Even multiple models have gained remarkable performance recently, they still encounter challenges. These challenges are related to the sparsity of user–item interaction data and thorough topic-driven user’s preference characterization. Moreover, these recent deep neural embedding-based recommendation models also suffer several limitations which are related to the capability of multi-viewed data embedding. Specifically, in the context of semantic-rich/network heterogeneity, they might be unable to fully incorporate the global structural representations of user–item interactions as well as associated data sources. Mainly motivated by remaining challenges, in this paper we propose novel topic-driven heterogeneous information network embedding-based technique which is aimed to effectively deal with the news recommendation, called as THIN4Rec. Our proposed THIN4Rec model enables to jointly capture the rich-semantic latent features of textual data and global structural representations of user–item interactions in the context of heterogeneous networks. The rich-semantic and structural representations of users and items are then used to improve the accuracy performance of news recommendation task. Extensive experiments in benchmark datasets prove the effectiveness of our works in comparison with recent state-of-the-art baselines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The authors declare that the data supporting the findings of this study are available within the article.

Notes

  1. Adressa dataset: http://reclab.idi.ntnu.no/dataset/.

  2. Adresseavisen news portal: https://www.adressa.no/.

  3. CoreNLP library for NLP (Java): https://stanfordnlp.github.io/CoreNLP/.

  4. StanfordNLP available language model list: https://stanfordnlp.github.io/stanfordnlp/models.html.

  5. BotXO: https://www.botxo.ai/.

  6. Pre-trained BERT for Norwegian: https://github.com/botxo/nordic_bert.

  7. LDA topic model (C/C + +): https://github.com/blei-lab/lda-c.

  8. Metapath2Vec model (Python—C/C + +): https://ericdongyx.github.io/metapath2vec/m2v.html.

References

  1. Zhang L, Zhang L (2021) Top-N recommendation algorithm integrated neural network. Neural Comput Appl 33:3881–3889

    Article  Google Scholar 

  2. Zhao X, Zhang Z, Bi X, & Sun Y, (2020) A new point-of-interest group recommendation method in location-based social networks. Neural Comput Appl, pp 1–12

  3. Raza, S., & Ding, C., News recommender system: a review of recent progress, challenges, and opportunities," Artificial Intelligence Review, pp. 1–5 2022.

  4. De Francisci Morales G, Gionis A, & Lucchese C (2012) From chatter to headlines: harnessing the real-time web for personalized news recommendation. in Proc. of the fifth ACM Int. Conf. on Web Search and Data Mining, Seattle, Washington, USA, pp 153–162

  5. Lu Z, Dou Z, Lian J, Xie X, & Yang Q, (2015) Content-based collaborative filtering for news topic recommendation. In Proc. of the 29th AAAI Int. Conf. on Artificial Intelligence, Austin, Texas, USA, vol. 29, no. 1

  6. Wang H, Zhang P, Lu T, Gu H, & Gu N, (2017) Hybrid recommendation model based on incremental collaborative filtering and content-based algorithms. In Proc. of the 21st IEEE Int. Conf. on Computer Supported Cooperative Work in Design (CSCWD), Wellington, New Zealand, pp 337–342

  7. Guo H, Tang R, Ye Y, Li Z, & He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In Proc. of the 26th Int. Conf. on Joint Conference on Artificial Intelligence, Melbourne, Australia, pp 1725–1731

  8. Hou X, Wang K, Zhong C, Wei Z (2021) St-trader: a spatial-temporal deep neural network for modeling stock market movement. IEEE/CAA J Autom Sinica 8(5):1015–1024

    Article  Google Scholar 

  9. Liu H, Chatterjee I, Zhou M, Lu XS, Abusorrah A (2020) Aspect-based sentiment analysis: a survey of deep learning methods. IEEE Trans Comput Social Syst 7(6):1358–1375

    Article  Google Scholar 

  10. Cheng HT, Koc L Harmsen, J Shaked T, Chandra T, Aradhye H & Shah H, (2016) Wide & deep learning for recommender systems. In Proc. of the 1st Workshop on Deep Learning for Recommender Systems, Seattle, Washington, USA, pp 7–10

  11. Wang H, Zhang F, Xie X, & Guo M (2018) DKN: deep knowledge-aware network for news recommendation. In Proc. of the 27th Int. Conf. on World Wide Web, Lyon, France, pp. 1835–1844, 2018.

  12. Zhang L, Liu P, & Gulla JA (2018) A deep joint network for session-based news recommendations with contextual augmentation. In Proc. of the 29th Int. Conf. on Hypertext and Social Media, Baltimore, Maryland, USA, pp 201–209

  13. Zhu Q, Zhou X, Song Z, Tan J, & Guo L, (2019) DAN: deep attention neural network for news recommendation. In Proc. of the 33th AAAI Int. Conf. on Artificial Intelligence, Honolulu, Hawaii, USA, vol. 33, no. 1, pp. 5973–5980, 2019.

  14. Sun Y, Han J, Yan X, Yu PS, & Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In Proc. of the 37th Int. Conf. on Very Large Data Bases, Seattle, Washington, USA, pp 992–1003

  15. Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37

    Article  Google Scholar 

  16. Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370

    Article  Google Scholar 

  17. Hu L, Li C, Shi C, Yang C, Shao C (2020) Graph neural news recommendation with long-term and short-term interest modeling. Inf Process Manage 57(2):102142

    Article  Google Scholar 

  18. Hu L, Xu S, Li C, Yang C, Shi C, Duan N, Zhou M (2020) Graph neural news recommendation with unsupervised preference disentanglement," in Proc. of the 58th Int. Conf. on Annual Meeting of the Association for Computational Linguistics, Seattle, Washington, USA, pp 4255–4264

  19. Wang Q, Liu X, Shang T, Liu Z, Yang H, Luo X (2022) Multi-constrained embedding for accurate community detection on undirected networks. IEEE Trans Netw Sci Eng 9(5):3675–3690

    Article  MathSciNet  Google Scholar 

  20. Hamilton WL, Ying R, & Leskovec J, (2017) Inductive representation learning on large graphs. In Proc. of the 31st Int. Conf. on Neural Information Processing Systems, Long Beach, California, USA, pp 1025–1035

  21. Kipf TN, & Welling M, (2017) Semi-supervised classification with graph convolutional networks. In Proc. of the fifth Int. Conf. on Learning Representations, Toulon, France

  22. Liu X, Yan M, Deng L, Li G, Ye X, Fan D (2021) Sampling methods for efficient training of graph convolutional networks: a survey. IEEE/CAA J Autom Sinica 9(2):205–234

    Article  MathSciNet  Google Scholar 

  23. Hong X, Zhang T, Cui Z, Yang J (2021) Variational gridded graph convolution network for node classification. IEEE/CAA J Autom Sinica 8(10):1697–1708

    Article  MathSciNet  Google Scholar 

  24. Devlin J, Chang MW, Lee K, & Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In Proc. of the Int. Conf. on North American Chapter of the Association for Computational Linguistics: human Language Technologies, Minneapolis, Minnesota, USA, pp. 4171–4186

  25. Mnih A, & Salakhutdinov RR, (2008) Probabilistic matrix factorization. In Proc. of the 22nd Int. Conf. on Neural Information Processing Systems

  26. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Article  MATH  Google Scholar 

  27. Karimi M, Jannach D, Jugovac M (2018) News recommender systems–Survey and roads ahead. Inf Process Manag 54(6):1203–1227

    Article  Google Scholar 

  28. Rendle S (2012) Factorization machines with libfm. ACM Trans Intell Syst Technol (TIST) 3(3):1–22

    Article  Google Scholar 

  29. Pham P, (2022) An attention-based adversarial disentangle heterogeneous embedding for improving node classification. Cybern Syst, pp 1–24,

  30. Zhao Z, Zhang X, Zhou H, Li C, Gong M, Wang Y (2020) HetNERec: heterogeneous network embedding based recommendation. Knowl-Based Syst 204:106218

    Article  Google Scholar 

  31. Fu X, Zhang J, Meng Z, King I, (2020) MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In Proc. of the Int. Conf. on the Web Conference, Taipei, Taiwan, pp 2331–2341

  32. Dong Y, Chawla NV, & Swami A, (2017) Metapath2vec: scalable representation learning for heterogeneous networks. In Proc. of the 23rd Int. Conf. on Knowledge Discovery and Data Mining, Halifax, NS, Canada, pp 135–144,

  33. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L Gomez AN, Polosukhin I (2017) Attention is all you need. In Proc. of the 31st Int. Conf. on Neural Information Processing Systems, Long Beach, California, USA, vol. 30

  34. Gulla J A, Zhang L, Liu,P, Özgöbek Ö, & Su X, (2017) The adressa dataset for news recommendation," in Proc. of the Int. Conf. on Web Intelligence, Leipzig, Germany, pp 1042–1048,

  35. Manning CD, Surdeanu M, Bauer J, Finkel JR, Bethard S, & McClosky D, (2014) The Stanford CoreNLP natural language processing toolkit. In Proc. of the 52nd Int. Conf. on Annual Meeting of the Association for Computational Linguistics: system Demonstrations, Baltimore, Maryland, USA, pp 55–60

  36. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  37. Gao S, Zhou M, Wang Y, Cheng J, Yachi H, Wang J (2018) Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans Neural Netwo LearnSyst 30(2):601–614

    Article  Google Scholar 

  38. Chen C, Lu N, Jiang B, Wang C (2021) A risk-averse remaining useful life estimation for predictive maintenance. IEEE/CAA J Autom Sinica 8(2):412–422

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam, for the support of time and facilities for this study.

Funding

This research is funded by Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, District 4, Ho Chi Minh City, Vietnam

    Tham Vo

Authors
  1. Tham Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tham Vo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vo, T. An integrated topic modeling and auto-encoder for semantic-rich network embedding and news recommendation. Neural Comput & Applic 35, 18681–18696 (2023). https://doi.org/10.1007/s00521-023-08697-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08697-5

Keywords

Search

Navigation