Skip to main content
SpringerLink
Log in

Next-Cart Recommendation by Utilizing Personalized Item Frequency Information in Online Web Portals

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In modern times, next-cart recommendation (NCR) has appeared as an excitable subject of research among researchers and is dominant in e-commerce. In NCR, a customer buys a set of products (termed a cart) in a particular interval of time. The broadly investigated categories like sequential or session-centered recommendation are majorly used to suggest the next product based on a sequence of products and are less complex when compared to NCR. The recommendation and sequential modeling are carried out by NCR by taking into consideration a sequence of carts. It has been observed that to carry out sequential modeling, the recurrent neural network (RNN) has been evidenced to be extremely efficient and thus utilized for NCR. After carrying out an extensive analysis of the actual world datasets, it is observed that personalized product frequency (PPF) information supplies two significant signals for NCR. But it is claimed that in the recommendation scene, RNNs which exist are not capable of capturing information about the frequency of an item in NCR. PPF can be described as the number of times that every product is bought by an individual. However, the concept of PPF has been overlooked in related studies. As a consequence, prevailing approaches are not able to fully explore the significant signals which exist in PPF. Although prevailing approaches like RNN-centered techniques exhibit robust representation ability, the experimental studies demonstrate that these methods are not able to recognize PPF. By keeping in mind this constraint of RNNs, a straightforward product frequency-centered k-nearest neighbors approach is proposed to straightforwardly incorporate these significant signals. The four actual world datasets are used for the assessment of the proposed technique. Apart from the simplicity of the technique, the proposed method performs better than existing NCR techniques and other deep learning techniques which employ RNNs.

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
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Aggarwal CC (2016) Recommender systems. https://doi.org/10.5555/2931100

  2. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Proceedings of the 19th international conference on World wide web, pp 811–820. https://doi.org/10.1145/1772690.1772773

  3. Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J (2018) Proceedings of the 27th international joint conference on artificial intelligence, pp 3926–3932. https://doi.org/10.5555/3304222.3304315

  4. Wang P, Guo J, Lan Y, Xu J, Wan S, Xueqi C (2015) Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 403–412. https://doi.org/10.1145/2766462.2767694

  5. Yu F, Liu Q, Wu S, Wang L, Tan T (2016) Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, pp 729–732. https://doi.org/10.1145/2911451.2914683

  6. Ning X, Desrosiers C, Karypis G (2015) Recommender systems handbook, pp 37–76. arXiv:2109.04584

  7. Ludewig M, Mauro N, Latifi S, Jannach D (2019) Proceedings of the 13th ACM conference on recommender systems, pp 462–466. https://doi.org/10.1145/3298689.3347041

  8. Wang C, Zhang M, Ma W, Liu Y, Ma S (2019) Proceedings of the world wide web conference, WWW 2019, pp 1977–1987. https://doi.org/10.1145/3308558.3313594

  9. Ren P, Chen Z, Li J, Ren Z, Ma J, Rijke M (2019) Proceedings of the AAAI conference on artificial intelligence, pp 4806–4813. https://doi.org/10.1609/aaai.v33i01.33014806

  10. Hu H, He X (2019) Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1491–1499. https://doi.org/10.1145/3292500.3330979

  11. Qin Y, Wang P, Li C (2021) Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 859–868. https://doi.org/10.1145/3404835.3462836

  12. Wang W, Cao L (2021) Interactive sequential basket recommendation by learning basket couplings and positive/negative feedback. ACM Trans Inf Syst (TOIS) 39:1–26. https://doi.org/10.1145/3444368

    Article  Google Scholar 

  13. Salampasis M, Siomos T, Katsalis A, Diamantaras K, Christantonis K, Delianidi M, Karaveli I (2021) 2021 13th international conference on machine learning and computing, pp 477–484. https://doi.org/10.1145/3457682.3457755

  14. Faggioli G, Polato M, Aiolli F (2020) Proceedings of the 28th ACM conference on user modeling, adaptation and personalization, pp 80–87. https://doi.org/10.1145/3340631.3394850

  15. Liu T, Yin X, Ni W (2020) Next basket recommendation model based on attribute-aware multi-level attention. IEEE Access 8:153,872–153,880. arXiv:2109.11654

  16. Leng Y, Yu L, Xiong J, Xu G (2020) International conference on database systems for advanced applications, vol 12114, pp 638–653. https://doi.org/10.1007/978-3-030-59419-0_39

  17. Zhang Y, Luo L, Zhang J, Lu Q, Wang Y, Wang Z (2020) Neural information processing—international conference on neural information processing, vol 1332, pp 746–753. https://www.ijcai.org/proceedings/2019/0389.pdf

  18. Zhang Y, Guo B, Wang Q, Sun Y, Yu Z (2020) 15th international conference on green, pervasive, and cloud computing, vol 12398, pp 171–185. https://doi.org/10.1007/978-3-030-64243-3_14

  19. Che B, Zhao P, Fang J, Zhao L, Sheng SV, Cui Z (2019) Inter-basket and intra-basket adaptive attention network for next basket recommendation. IEEE Access 7:80,644–80,650. arXiv:2109.11654

  20. Wang P, Zhang O, Niu S, Guo J (2019) Modeling temporal dynamics of users’ purchase behaviors for next basket prediction. J Comput Sci Technol 34:1230–1240. https://doi.org/10.1007/s11390-019-1972-2

    Article  Google Scholar 

  21. Dhawan S, Singh K, Batra A (2021) International conference on mobile radio communications and 5G networks, pp 73–85. https://doi.org/10.1007/978-981-15-7130-5_6

  22. Dhawan S, Singh K, Batra A (2021) Recent innovations in computing—proceedings of ICRIC 2020, pp 151–163. https://doi.org/10.1007/978-981-15-8297-4_13

  23. Dhawan S, Singh K, Batra A (2021) Intelligent computing and communication systems, pp 363–370. https://doi.org/10.1007/978-981-16-1295-4_37

  24. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook, pp 1–35. https://doi.org/10.1007/978-0-387-85820-3_1

  25. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22:143–177. https://doi.org/10.1145/963770.963776

    Article  Google Scholar 

  26. Konstan AJ, Miller NB, Maltz D, Herlocker LJ, Gordon RL, Riedl J (1997) Grouplens: applying collaborative filtering to usenet news. Commun ACM 40:77–87. https://doi.org/10.1145/245108.245126

    Article  Google Scholar 

  27. Koren Y (2008) Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 426–434. https://doi.org/10.1145/1401890.1401944

  28. Bell R, Koren Y, Volinsky C (2008) Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 95–104. https://doi.org/10.1145/1281192.1281206

  29. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Proceedings of the 43rd international ACM SIGIR conference on research & development in information retrieval, pp 639–648. https://doi.org/10.1145/3397271.3401063

  30. Liang D, Krishnan GR, Hoffman DM, Jebara T (2018) Proceedings of the 2018 world wide web conference on world wide web, pp 689–698. https://doi.org/10.1145/3178876.3186150

  31. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Proceedings of the 26th international conference on world wide web, pp 173–182. https://doi.org/10.1145/3038912.3052569

  32. Dhawan S, Kulvinder S, Rabaea A, Batra A (2022) Improvedgcn: an efficient and accurate recommendation system employing lightweight graph convolutional networks in social media. Electron Commerce Res Appl 55:101,191. https://doi.org/10.1016/j.elerap.2022.101191

    Article  Google Scholar 

  33. He R, McAuley J (2016) Proceedings of the 2016 IEEE 16th international conference on data mining, pp 191–200. https://doi.org/10.1109/ICDM.2016.0030

  34. Kang WC, McAuley J (2018) Proceedings of the 2018 IEEE international conference on data mining (ICDM), pp 197–206. https://doi.org/10.1109/ICDM.2018.00035

  35. He R, Kang WC, McAuley J (2018) Proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI-18), pp 5264–5268. https://www.ijcai.org/Proceedings/2018/0734.pdf

  36. Yuan F, He X, Jiang H, Guo G, Xiong J, Xu Z, Xiong Y (2020) Proceedings of the web conference 2020, pp 303–313. https://doi.org/10.1145/3366423.3380116

  37. Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. User Model User-Adap Inter 28:331–390. https://doi.org/10.1007/s11257-018-9209-6

    Article  Google Scholar 

  38. Jannach D, Ludewig M (2017) Proceedings of the eleventh ACM conference on recommender systems, pp 306–310. https://doi.org/10.1145/3109859.3109872

  39. Dhawan S, Kulvinder S, Rabaea A, Batra A (2021) Session centered recommendation utilizing future contexts in social media. Analele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica 29:91–104. https://doi.org/10.2478/auom-2021-0036

    Article  MathSciNet  MATH  Google Scholar 

  40. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Proceedings of the 4th international conference on learning representations, ICLR 2016. arXiv:1511.06939v4

  41. Bai T, Nie JY, Zhao WX, Zhu Y, Du P, Wen JR (2018) Proceedings of the 41st international ACM SIGIR conference on research & development in information retrieval, pp 1201–1204. https://doi.org/10.1145/3209978.3210129

  42. Pascanu R, Mikolov T, Bengio Y (2013) Proceedings of the 30th international conference on machine learning, vol 28, pp 1310–1318. https://doi.org/10.5555/3042817.3043083

  43. Blum A, Rivest LR (1988) NIPS’88: Proceedings of the 1st international conference on neural information processing systems, pp 494–501. https://doi.org/10.5555/93025.93033

  44. Hong C, Yu J, Zhang J, Jin X, Lee KH (2018) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inf 15(7):3952–3961

    Article  Google Scholar 

  45. Yu J, Tao D, Wang M, Rui Y (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybernet 45(4):767–779. https://doi.org/10.1109/TCYB.2014.2336697

    Article  Google Scholar 

  46. Yu J, Tan M, Zhang H, Rui Y, Tao D (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell 44(2):563–578. https://doi.org/10.1109/TPAMI.2019.2932058

    Article  Google Scholar 

  47. Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670. https://doi.org/10.1109/TIP.2015.2487860

    Article  MathSciNet  MATH  Google Scholar 

  48. Hong C, Yu J, Tao D, Wang M (2014) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751. https://doi.org/10.1109/TIE.2014.2378735

    Article  Google Scholar 

  49. Anderson A, Kumar R, Tomkins A, Vassilvitskii S (2014) Proceedings of the 23rd international conference on World Wide Web, pp 419–430. https://doi.org/10.1145/2566486.2568018

  50. Benson RA, Kumar R, Tomkins A (2016) in Proceedings of the 25th international conference companion on World Wide Web, pp 519–529. https://doi.org/10.1145/2872427.2883024

  51. Dawes J, Meyer-Waarden L, Driesener C (2015) Has brand loyalty declined? a longitudinal analysis of repeat purchase behavior in the UK and the USA. J Bus Res 68:425–432

    Article  Google Scholar 

  52. Jacoby J, Kyner BD (1973) Brand loyalty vs. repeat purchasing behavior. J Mark Res 10:1–9. https://doi.org/10.1177/002224377301000101

    Article  Google Scholar 

  53. Wan M, Wang D, Liu J, Bennett P, McAuley J (2018) Proceedings of the 27th ACM international conference on information and knowledge management, pp 1133–1142. https://doi.org/10.1145/3269206.3271786

  54. Siegelmann TH, Sontag DE (1992) Proceedings of the fifth annual workshop on Computational learning theory, pp 440–449. https://doi.org/10.1145/130385.130432

  55. Bottou L (2010) in Proceedings of the 19th international conference on computational statistics—COMPSTAT’2010, pp 177–186. https://doi.org/10.1007/978-3-7908-2604-3_16

  56. Greff K, Srivastava RK, Koutnik J, Steunebrink RB, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28:2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924

    Article  MathSciNet  Google Scholar 

  57. Cho K, Merri enboer BV, Bahdanau D, Bengio Y (2014) Proceedings of SSST-8, eighth workshop on syntax, semantics and structure in statistical translation, pp 103–111. arXiv:1409.1259v2

  58. Mandic PD, Chambers AJ (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley, New York. https://doi.org/10.1002/047084535X.fmatter_indsub

    Book  Google Scholar 

  59. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  60. Kingma PD, Ba J (2014) Proceedings of the 3rd international conference on learning representations, ICLR 2015. arXiv:1412.6980v9

  61. Goodfellow JI, Vinyals O, Saxe MA (2014) Proceedings of the 3rd international conference on learning representations, ICLR 2015. https://doi.org/10.48550/arXiv.2012.06898

  62. Hu W, Xiao L, Pennington J (2020) Proceedings of the 8rd international conference on learning representations, ICLR 2020. arXiv:2001.05992

  63. Koren Y (2009) Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 447–456. https://doi.org/10.1145/1557019.1557072

  64. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Proceedings of the 10th international conference on World Wide Web, pp 285–295. https://doi.org/10.1145/371920.372071

  65. He X, Chen T, Kan MY, Chen X (2015) Proceedings of the 24th ACM international conference on information and knowledge management, pp 1661–1670. https://doi.org/10.1145/2806416.2806504

  66. Wang G, Giannakis BG, Chen J (2019) Learning ReLU networks on linearly separable data: algorithm, optimality, and generalization. IEEE Trans Signal Process 67:2357–2370. https://doi.org/10.1109/TSP.2019.2904921

    Article  MathSciNet  MATH  Google Scholar 

  67. Sharma M, Karypis G (2019) Proceedings of the the world wide web conference, pp 3223–3229. https://doi.org/10.1145/3308558.3313736

Download references

Funding

The authors have no relevant financial or non-financial interests to disclose. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University Institute of Engineering and Technology, Kurukshetra University, Thanesar, India

    Dhawan Sanjeev, Kulvinder Singh & Amit Batra

  2. Faculty of Mechanical, Industrial and Maritime Engineering, “Ovidius” University of Constanta, Constanţa, Romania

    Eduard-Marius Craciun

  3. Department of Mathematics and Computer Science, Faculty of Sciences, Technical University of Cluj-Napoca N.U.C.B.M., Cluj-Napoca, Romania

    Adrian Rabaea

Authors
  1. Dhawan Sanjeev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kulvinder Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eduard-Marius Craciun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adrian Rabaea
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amit Batra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian Rabaea.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

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

Sanjeev, D., Singh, K., Craciun, EM. et al. Next-Cart Recommendation by Utilizing Personalized Item Frequency Information in Online Web Portals. Neural Process Lett 55, 9409–9434 (2023). https://doi.org/10.1007/s11063-023-11207-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11207-2

Keywords

Search

Navigation