Skip to main content
SpringerLink
Log in

Diversification in session-based news recommender systems

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites, most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the active session. Neighborhood-based session-based recommenders have been shown to be highly effective compared to more sophisticated approaches. In this study, we propose scenarios to make these session-based recommender systems diversity-aware and to address the filter bubble phenomenon. The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information. The results of applying the proposed scenarios show that these diversification scenarios improve the diversity measures in these session-based recommender systems based on four news datasets.

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

Similar content being viewed by others

Notes

  1. Inverse Document Frequency.

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

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

  4. The pre-processed version of the datasets are available upon request. The last two datasets were obtained from Roularta Media Group, a Belgian multimedia group.

  5. The source code is available at https://github.com/alirezagharahi/d_SBRS.

References

  1. Adomavicius G, Kwon Y (2011) Maximizing aggregate recommendation diversity: a graph-theoretic approach. In: Proc of the 1st international workshop on novelty and diversity in recommender systems (diveRS 2011), Citeseer, pp 3–10

  2. Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214–226

  3. Ekstrand MD, Harper FM, Willemsen MC, Konstan JA (2014) User perception of differences in recommender algorithms. In: Proceedings of the 8th ACM Conference on Recommender systems, pp 161–168

  4. Gabriel De Souza PM, Jannach D, Da Cunha AM (2019) Contextual hybrid session-based news recommendation with recurrent neural networks. IEEE Access 7:169185–169203

    Article  Google Scholar 

  5. Garg D, Gupta P, Malhotra P, Vig L, Shroff G (2019) Sequence and time aware neighborhood for session-based recommendations: Stan. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1069–1072

  6. Gharahighehi A, Vens C (2020) Extended bayesian personalized ranking based on consumption behavior. In: Postproceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019). Springer

  7. Gharahighehi A, Vens C (2020) Making session-based news recommenders diversity-aware. In: OHARS’20: Workshop on online misinformation- and harm-aware recommender systems

  8. Gharahighehi A, Vens C (2021) Personalizing diversity versus accuracy in session-based recommender systems. SN Comput Sci 2(1):1–12

    Article  Google Scholar 

  9. Gharahighehi A, Vens C, Pliakos K (2020) Multi-stakeholder news recommendation using hypergraph learning. In: ECML PKDD 2020 Workshops. Springer International Publishing, Cham, pp 531–535

  10. Gharahighehi A, Vens C, Pliakos K (2021) Fair multi-stakeholder news recommender system with hypergraph ranking. Inform Process Manage 58(5):102663. https://doi.org/10.1016/j.ipm.2021.102663

    Article  Google Scholar 

  11. Gulla JA, Zhang L, Liu P, Özgöbek Ö, Su X (2017) The adressa dataset for news recommendation. In: Proceedings of the international conference on web intelligence, pp 1042– 1048

  12. Helberger N (2019) On the democratic role of news recommenders. Digital Journalism 7 (8):993–1012

    Article  Google Scholar 

  13. Hidasi B, Karatzoglou A (2018) Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 843–852

  14. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv:1511.06939

  15. 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, ACM, pp 241– 248

  16. Jambor T, Wang J (2010) Optimizing multiple objectives in collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems, pp 55–62

  17. Jannach D, Ludewig M (2017) When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp 306–310

  18. Jugovac M, Jannach D, Karimi M (2018) Streamingrec: a framework for benchmarking stream-based news recommenders. in: Proceedings of the 12th ACM Conference on Recommender Systems, pp 269–273

  19. Jugovac M, Jannach D, Lerche L (2017) Efficient optimization of multiple recommendation quality factors according to individual user tendencies. Expert Syst Appl 81:321–331

    Article  Google Scholar 

  20. Kaminskas M, Bridge D (2016) Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans Interac Intell Syst (TiiS) 7(1):1–42

    Google Scholar 

  21. Karimi M, Jannach D, Jugovac M (2018) News recommender systems–survey and roads ahead. Inform Process Manage 54(6):1203–1227

    Article  Google Scholar 

  22. Kelly JP, Bridge D (2006) Enhancing the diversity of conversational collaborative recommendations: a comparison. Artif Intell Rev 25(1-2):79–95

    Article  Google Scholar 

  23. Kouki P, Fountalis I, Vasiloglou N, Cui X, Liberty E, Al Jadda K (2020) From the lab to production: a case study of session-based recommendations in the home-improvement domain. In: Fourteenth ACM conference on recommender systems, pp 140–149

  24. Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1419–1428

  25. Linden G, Smith B, York J (2003) Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7(1):76–80

  26. Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. User Model User-Adap Inter 28(4-5):331–390

    Article  Google Scholar 

  27. Ludewig M, Mauro N, Latifi S, Jannach D (2019) Performance comparison of neural and non-neural approaches to session-based recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp 462–466

  28. Ludewig M, Mauro N, Latifi S, Jannach D (2020) Empirical analysis of session-based recommendation algorithms. User Model User-Adap Inter, pp 1–33

  29. Nguyen TT, Hui PM, Harper FM, Terveen L, Konstan JA (2014) Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd international conference on World wide web, pp 677–686

  30. Pariser E (2011) The filter bubble: What the Internet is hiding from you. Penguin UK

  31. Said A, Fields B, Jain BJ, Albayrak S (2013) User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm. In: Proceedings of the 2013 conference on Computer supported cooperative work, pp 1399–1408

  32. Shi Y, Zhao X, Wang J, Larson M, Hanjalic A (2012) Adaptive diversification of recommendation results via latent factor portfolio. In: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp 175–184

  33. Smyth B, McClave P (2001) Similarity vs. diversity. In: International conference on case-based reasoning, Springer, pp 347–361

  34. Steck H (2018) Calibrated recommendations. In: Proceedings of the 12th ACM conference on recommender systems, pp 154–162

  35. Su R, Yin L, Chen K, Yu Y (2013) Set-oriented personalized ranking for diversified top-n recommendation. In: Proceedings of the 7th ACM conference on Recommender systems, pp 415– 418

  36. Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender system. In: Proceedings of the fifth ACM conference on Recommender systems, pp 109– 116

  37. Vargas S, Castells P, Vallet D (2011) Intent-oriented diversity in recommender systems. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp 1211–1212

  38. Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 346–353

Download references

Acknowledgements

This work was executed within the imec.icon project NewsButler, a research project bringing together academic researchers (KU Leuven, VUB) and industry partners (Roularta Media Group, Bothrs and ML6). The NewsButler project is co-financed by imec and receives project support from Flanders Innovation & Entrepreneurship (project nr. HBC.2017.0628). The authors also acknowledge support from the Flemish Government (AI Research Program).

Author information

Authors and Affiliations

  1. Itec, imec Research Group at KU Leuven, Kortrijk, Belgium

    Alireza Gharahighehi & Celine Vens

  2. KU Leuven, Campus KULAK, Department of Public Health and Primary Care, Kortrijk, Belgium

    Alireza Gharahighehi & Celine Vens

Authors
  1. Alireza Gharahighehi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Celine Vens
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Gharahighehi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gharahighehi, A., Vens, C. Diversification in session-based news recommender systems. Pers Ubiquit Comput 27, 5–15 (2023). https://doi.org/10.1007/s00779-021-01606-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-021-01606-4

Keywords

Search

Navigation