Skip to main content
SpringerLink
Log in

PGT: news recommendation coalescing personal and global temporal preferences

  • Regular paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Given sequential news watch logs of users, how can we accurately recommend news articles? Compared to other items (e.g., movies and e-commerce products) for a recommendation, the worth of news articles decays quickly and massive news articles are published every second. Moreover, people frequently watch popular news articles regardless of their personal tastes to browse remarkable events at a specific time. Current state-of-the-art methods, designed for other target item domains, show low performance when they are used for news recommendation because of these peculiarities of news articles. In this paper, we propose PGT (News Recommendation Coalescing Personal and Global Temporal Preferences), an accurate news recommendation method designed with consideration of the above properties of news articles. PGT sufficiently reflects users’ behaviors by utilizing latent features extracted from both personal and global temporal preferences. Furthermore, we propose an attention-based architecture to extract adequate coalesced features from both of the preferences. We carefully tune each component of PGT to find optimal architecture. Experimental results show that PGT provides the most accurate news recommendation, giving the state-of-the-art accuracy.

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.

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

Similar content being viewed by others

Notes

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

  2. https://www.kaggle.com/gspmoreira/news-portal-user-interactions-by-globocom.

References

  1. Kang D, Han D, Park N, Kim S, Kang U, Lee S (2014) Eventera: real-time event recommendation system from massive heterogeneous online media. In: 2014 IEEE international conference on data mining workshops, ICDM Workshops 2014, Shenzhen, China, December 14, 2014, pp 1211–1214

  2. Okura S, Tagami Y, Ono S, Tajima A (2017) Embedding-based news recommendation for millions of users. In: SIGKDD

  3. Park K, Lee J, Choi J (2017) Deep neural networks for news recommendations. In: CIKM, 2017

  4. Khattar D, Kumar V, Varma V, Gupta M (2018) Weave&rec: a word embedding based 3-d convolutional network for news recommendation. In: CIKM

  5. Wu C, Wu F, An M, Huang J, Huang Y, Xie X (2019) NPA: neural news recommendation with personalized attention. In: SIGKDD

  6. Khattar D, Kumar V, Varma V, Gupta M (2018) HRAM: a hybrid recurrent attention machine for news recommendation. In: CIKM

  7. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc., 57 Morehouse Lane, Red Hook, NY, United States

    Google Scholar 

  8. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: ICLR, 2016

  9. Hidasi B, Quadrana M, Karatzoglou A, Tikk D (2016) Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM conference on recommender systems

  10. Jeon H, Koo B, Kang U (2019) Data context adaptation for accurate recommendation with additional information. In: IEEE BigData

  11. Wang H, Zhang F, Xie X, Guo M (2018) DKN: deep knowledge-aware network for news recommendation. In: WWW

  12. Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP

  13. Koo B, Jeon H, Kang U (2020) Accurate news recommendation coalescing personal and global temporal preferences. In: Advances in knowledge discovery and data mining—24th Pacific-Asia conference, PAKDD 2020, Singapore, May 11–14, 2020. Proceedings, Part I, pp 78–90

  14. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: ICML

  15. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: ICLR

  16. Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Màrquez L, Callison-Burch C, Su J, Pighin D, Marton Y (eds) EMNLP

  17. Gulla JA, Zhang L, Liu P, Özgöbek Ö, Su X (2017) The adressa dataset for news recommendation. In: WI

  18. de Souza Pereira Moreira G, Ferreira F, da Cunha AM (2018) News session-based recommendations using deep neural networks. In: DLRS@RecSys

  19. Řehůřek R, Sojka P (2010) Software framework for topic modelling with large Corpora. In: LREC

  20. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI. Morgan Kaufmann Publishers Inc

  21. Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: ICLR

  22. Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22:400–407

    Article  MathSciNet  Google Scholar 

  23. Duchi JC, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

    MathSciNet  MATH  Google Scholar 

  24. Montúfar GF, Pascanu R, Cho K, Bengio Y (2014) On the number of linear regions of deep neural networks. In: NIPS

  25. Zagoruyko S, Komodakis N (2016) Wide residual networks. In: BMVC

Download references

Acknowledgements

The Institute of Engineering Research at Seoul National University provided research facilities for this work. The ICT at Seoul National University provides research facilities for this study. This work was also supported by DeepTrade Inc.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea

    Bonhun Koo, Hyunsik Jeon & U. Kang

  2. DeepTrade Inc., Seoul, South Korea

    U. Kang

Authors
  1. Bonhun Koo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyunsik Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. U. Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to U. Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koo, B., Jeon, H. & Kang, U. PGT: news recommendation coalescing personal and global temporal preferences. Knowl Inf Syst 63, 3139–3158 (2021). https://doi.org/10.1007/s10115-021-01618-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-021-01618-9

Keywords

Search

Navigation