Skip to main content
SpringerLink
Log in

Data, Recommendation Techniques, and View (DRV) Model for Online Transaction

  • Conference paper
  • First Online:
Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 721))

  • 320 Accesses

Abstract

With the development of information technology, online transactions, including E-commerce, have been developed. Accordingly, recommendation systems were developed to facilitate customer preferences and increase business revenue. In this paper, our analysis shows that each of these systems was implemented to facilitate the recommendation process of a specific product or service category and applied to a dedicated context. The issue here is if the business provides more than one category of products and/or services it needs to utilize more than one approach to have an effective recommendation process. That would make it more complicated to implement and with a high cost. In addition, each of these systems was developed to overcome a specific problem. There is no guarantee that the system developed to address a dedicated problem could overcome the other problems. Examples of these problems include cold-start, data sparsity, accuracy, and diversity. In this paper, we develop Data, Recommendation Technique, and View (DRV) model. We consider this model to be a foundation for a generic framework to develop recommendation systems that overcome the issues mentioned.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Stanujkic, D., Karabasevic, D., Maksimovic, M., Popovic, G., Brzakovic, M.: Evaluation of the e-commerce development strategies. Quaestus 1, 144–152 (2019)

    Google Scholar 

  2. Abadi, S., et al.: Design of online transaction model on traditional industry in order to increase turnover and benefits. Int. J. Eng. Technol. 7(2.27), 231–237 (2018)

    Article  Google Scholar 

  3. Khan, M.K., Nawaz, M.R., Ishaq, M.I., Tariq, M.I.: Product versus service: old myths versus new realities. J. Basic Appl. Sci. Res. 4(1), 15–20 (2014)

    Google Scholar 

  4. Parry, G., Newnes, L., Huang, X.: Goods, products and services. In: Macintyre, M., Parry, G., Angelis, J. (eds.) Service Design and Delivery, pp. 19–29. Springer US, Boston, MA (2011). https://doi.org/10.1007/978-1-4419-8321-3_2

    Chapter  Google Scholar 

  5. Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015)

    Article  Google Scholar 

  6. Barbosa, C.E., Oliveira, J., Maia, L., Souza, J.: MISIR: recommendation systems in a knowledge management scenario. Int. J. Continuing Eng. Educ. Life-Long Learn. 20, 02/01 (2010)

    Google Scholar 

  7. Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)

    Article  Google Scholar 

  8. Mezni, H., Abdeljaoued, T.: A cloud services recommendation system based on fuzzy formal concept analysis. Data Knowl. Eng. 116, 100–123 (2018)

    Article  Google Scholar 

  9. Hsieh, M.-Y., Weng, T.-H., Li, K.-C.: A keyword-aware recommender system using implicit feedback on Hadoop. J. Parallel Distrib. Comput. 116, 63–73 (2018)

    Article  Google Scholar 

  10. Zhang, S., Zhang, S., Yen, N.Y., Zhu, G.: The recommendation system of micro-blog topic based on user clustering. Mob. Netw. Appl. 22(2), 228–239 (2016)

    Article  Google Scholar 

  11. Chai, Z.-Y., Li, Y.-L., Han, Y.-M., Zhu, S.-F.: Recommendation system based on singular value decomposition and multi-objective immune optimization. IEEE Access 7, 6060–6071 (2018)

    Article  Google Scholar 

  12. Gan, M., Jiang, R.: Constructing a user similarity network to remove adverse influence of popular objects for personalized recommendation. Expert Syst. Appl. 40(10), 4044–4053 (2013)

    Article  Google Scholar 

  13. Xu, W., Sun, J., Ma, J., Du, W.: A personalized information recommendation system for R&D project opportunity finding in big data contexts. J. Netw. Comput. Appl. 59, 362–369 (2016)

    Article  Google Scholar 

  14. Yun, Y., Hooshyar, D., Jo, J., Lim, H.: Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review. J. Inf. Sci. 44(3), 331–344 (2018)

    Article  Google Scholar 

  15. Liu, C.-L., Wu, X.-W.: Fast recommendation on latent collaborative relations. Knowl.-Based Syst. 109, 25–34 (2016)

    Article  Google Scholar 

  16. Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178(1), 37–51 (2008)

    Article  Google Scholar 

  17. Achakulvisut, T., Acuna, D.E., Ruangrong, T., Kording, K.: Science concierge: a fast content-based recommendation system for scientific publications. PLoS ONE 11(7), e0158423 (2016)

    Article  Google Scholar 

  18. Wang, D., Liang, Y., Xu, D., Feng, X., Guan, R.: A content-based recommender system for computer science publications. Knowl.-Based Syst. 157, 1–9 (2018)

    Article  Google Scholar 

  19. Ochirbat, A., et al.: Hybrid occupation recommendation for adolescents on interest, profile, and behavior. Telematics Inform. 35(3), 534–550 (2018)

    Article  Google Scholar 

  20. Kanavos, A., Iakovou, S.A., Sioutas, S., Tampakas, V.: Large scale product recommendation of supermarket ware based on customer behaviour analysis. Big Data Cogn. Comput. 2(2), 11 (2018)

    Article  Google Scholar 

  21. Scholz, M., Dorner, V., Schryen, G., Benlian, A.: A configuration-based recommender system for supporting e-commerce decisions. Eur. J. Oper. Res. 259(1), 205–215 (2017)

    Article  MATH  Google Scholar 

  22. Mariappan, P., Viswanathan, V., Čepová, L.: Application of semantic analysis and LSTM-GRU in developing a personalized course recommendation system. Appl. Sci. 12(21), 10792 (2022)

    Article  Google Scholar 

  23. An, H.-W., Moon, N.: Design of recommendation system for tourist spot using sentiment analysis based on CNN-LSTM. J. Ambient. Intell. Humaniz. Comput. 13(3), 1653–1663 (2019)

    Article  Google Scholar 

  24. Kiruthika, N.S., Thailambal, D.G.: Dynamic light weight recommendation system for social networking analysis using a hybrid LSTM-SVM classifier algorithm. Opt. Mem. Neural Networks 31(1), 59–75 (2022)

    Article  Google Scholar 

  25. Fu, M., Qu, H., Moges, D., Lu, L.: Attention based collaborative filtering. Neurocomputing 311, 88–98 (2018)

    Article  Google Scholar 

  26. Wang, Y., Deng, J., Gao, J., Zhang, P.: A hybrid user similarity model for collaborative filtering. Inf. Sci. 418, 102–118 (2017)

    Article  Google Scholar 

  27. Khalaji, M., Dadkhah, C., Gharibshah, J.: Hybrid movie recommender system based on resource allocation. arXiv preprint arXiv:2105.11678 (2021)

  28. Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis, pp. 1041–1042 (2008)

    Google Scholar 

  29. Shafqat, W., Byun, Y.-C.: A context-aware location recommendation system for tourists using hierarchical LSTM model. Sustainability (Basel, Switzerland) 12(10), 4107 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Australian Catholic University, Canberra, Australia

    Abdussalam Ali

  2. Asia Pacific International College (APIC), Parramatta, Australia

    Abdussalam Ali

  3. The University of Sydney, Sydney, Australia

    Waleed Ibrahim

  4. Central Queensland University, Rockhampton, Australia

    Waleed Ibrahim

  5. Western Sydney University, Sydney, Australia

    Sabreena Zoha

Authors
  1. Abdussalam Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Waleed Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sabreena Zoha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdussalam Ali .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Farmington Hills, MI, USA

    Kevin Daimi

  2. Asia Pacific International College, Sydney, NSW, Australia

    Abeer Al Sadoon

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ali, A., Ibrahim, W., Zoha, S. (2023). Data, Recommendation Techniques, and View (DRV) Model for Online Transaction. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_12

Download citation

Publish with us

Policies and ethics

Search

Navigation