Skip to main content
SpringerLink
Log in

Improving Session Based Recommendation by Diversity Awareness

  • Conference paper
  • First Online:
Advances in Computational Intelligence Systems (UKCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1043))

Included in the following conference series:

Abstract

Recommender systems help users to discover and filter new and interesting products based on their preferences. Session-Based Recommender systems are powerful tools for anonymous e-commerce visitors to understand their behaviours and recommend useful products. Diversity in the recommendations is an important parameter due to increasing the opportunity of recommending new and less similar items that users interacted. Effect of diversity has been investigated in many works for the collaborative filtering-based Recommender systems. However, for session-based Recommender systems, exploring the effect of diversity is still an open area. In this paper, we propose an approach to calculate the diversity level of the items in the session logs and analyse the effect of diversity level on the session-based recommendation. In order to test the impact of diversity awareness, we propose a sequential Item-KNN recommendation model. The final recommendation list is created as a contribution of the interacted items in the session that depends on the diversity level between last interacted item of the session. We conduct several experiments to validate our diversity aware model on a real-world dataset. The results show that diversity awareness in the sessions helps to improve the performance of Recommender system in terms of recall and precision evaluation metrics. Also, the proposed method can be applied to other sequential Recommender system methods, including deep-learning based Recommender systems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.freshrelevance.com/.

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 6, 734–749 (2005)

    Article  Google Scholar 

  2. Bader-El-Den, M.: Self-adaptive heterogeneous random forest. In: 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), pp. 640–646. IEEE (2014)

    Google Scholar 

  3. Bader-El-Den, M., Teitei, E., Perry, T.: Biased random forest for dealing with the class imbalance problem. IEEE Trans. Neural Networks Learn. Syst. (2018)

    Google Scholar 

  4. Besbes, O., Gur, Y., Zeevi, A.: Optimization in online content recommendation services: beyond click-through rates. Manuf. Serv. Oper. Manag. 18(1), 15–33 (2015)

    Article  Google Scholar 

  5. Castagnos, S., Brun, A., Boyer, A.: When diversity is needed... but not expected! In: International Conference on Advances in Information Mining and Management, pp. 44–50. IARIA XPS Press (2013)

    Google Scholar 

  6. Di Noia, T., Ostuni, V.C., Rosati, J., Tomeo, P., Di Sciascio, E.: An analysis of users’ propensity toward diversity in recommendations. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 285–288. ACM (2014)

    Google Scholar 

  7. Diedhiou, C., Carpenter, B., Esmeli, R.: Comparison of platforms for recommender algorithm on large datasets. In: 7th Imperial College Computing Student Workshop, pp. 4–1. Schloss Dagstuhl–Leibniz Center for Informatics (2019)

    Google Scholar 

  8. Diedhiou, C., Carpenter, B., Shafi, A., Sarkar, S., Esmeli, R., Gadsdon, R.: Performance comparison of a parallel recommender algorithm across three hadoop-based frameworks. In: 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pp. 380–387. IEEE (2018)

    Google Scholar 

  9. Fleder, D.M., Hosanagar, K.: Recommender systems and their impact on sales diversity. In: Proceedings of the 8th ACM Conference on Electronic Commerce, pp. 192–199. ACM (2007)

    Google Scholar 

  10. Hamedani, E.M., Kaedi, M.: Recommending the long tail items through personalized diversification. Knowl.-Based Syst. 164, 348–357 (2019)

    Article  Google Scholar 

  11. He, R., McAuley, J.: Fusing similarity models with markov chains for sparse sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 191–200. IEEE (2016)

    Google Scholar 

  12. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

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

  14. Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241–248. ACM (2016)

    Google Scholar 

  15. Jannach, D., Lerche, L., Jugovac, M.: Adaptation and evaluation of recommendations for short-term shopping goals. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 211–218. ACM (2015)

    Google Scholar 

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

    Google Scholar 

  17. Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659–667. ACM (2013)

    Google Scholar 

  18. Karabadji, N.E.I., Beldjoudi, S., Seridi, H., Aridhi, S., Dhifli, W.: Improving memory-based user collaborative filtering with evolutionary multi-objective optimization. Expert Syst. Appl. 98, 153–165 (2018)

    Article  Google Scholar 

  19. Kunaver, M., Požrl, T.: Diversity in recommender systems-a survey. Knowl.-Based Syst. 123, 154–162 (2017)

    Article  Google Scholar 

  20. Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 210–217. ACM (2010)

    Google Scholar 

  21. Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31–40. ACM (2010)

    Google Scholar 

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

    Article  Google Scholar 

  25. Mohasseb, A., Bader-El-Den, M., Cocea, M.: Question categorization and classification using grammar based approach. Inf. Process. Manag. 54(6), 1228–1243 (2018)

    Article  Google Scholar 

  26. Moreira, G.D.S.P., Jannach, D., da Cunha, A.M.: Contextual hybrid session-based news recommendation with recurrent neural networks. arXiv preprint arXiv:1904.10367 (2019)

  27. Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. ACM Comput. Surv. (CSUR) 51(4), 66 (2018)

    Article  Google Scholar 

  28. Vargas, S.: New approaches to diversity and novelty in recommender systems. In: Fourth BCS-IRSG symposium on Future Directions in Information Access (FDIA 2011), Koblenz, vol. 31 (2011)

    Google Scholar 

  29. Wang, W., Yin, H., Sadiq, S., Chen, L., Xie, M., Zhou, X.: Spore: A sequential personalized spatial item recommender system. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 954–965. IEEE (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Portsmouth, Portsmouth, PO1 2UP, UK

    Ramazan Esmeli, Mohamed Bader-El-Den & Hassana Abdullahi

Authors
  1. Ramazan Esmeli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Bader-El-Den
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassana Abdullahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramazan Esmeli .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Chenguang Yang

  4. School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK

    Alexander Gegov

  5. School of Computing, University of Portsmouth, Portsmouth, UK

    Dalin Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Esmeli, R., Bader-El-Den, M., Abdullahi, H. (2020). Improving Session Based Recommendation by Diversity Awareness. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_27

Download citation

Publish with us

Policies and ethics

Search

Navigation