A review of techniques used in e-commerce recommendation system

Research Article
Open access

A review of techniques used in e-commerce recommendation system

Ke Yan 1
  • 1 University of Liverpool, Liverpool, UK    
  • *corresponding author
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

In the age of the internet, the explosive growth of information makes the e-commerce platform users need more and more requirements for the shopping experience. Recommendation systems are developed to retrieve information efficiently and provide a personalised shopping experience for users. There are three types of algorithms that are most frequently employed in recommendation systems: content-based filtering, collaborative filtering, and hybrid techniques. In this paper, content-based filtering and collaborative filtering are firstly explained in terms of working principles, limitations, and advantages. Then, some hybrid techniques that combine content-based filtering and collaborative filtering are proposed to overcome the problems of these two conventional methods. It is found that while collaborative filtering may struggle with scalability, sparsity, and cold start issues, content-based filtering may have quality issues and a lack of variation. The hybrid techniques are introduced to address the drawbacks of these two algorithms. It is, however, more complex and requires more memory resources to use hybrid techniques. Implementing deep learning in recommendation systems would be a promising area in the future. Through the analysis above, this paper would provide a systematic review of the development of the recommendation system and inform the future study. Students or researchers who are interested in big data can quickly grasp the concepts of techniques used in e-commerce recommendation systems as well as the benefits and drawbacks of different techniques.

Keywords:

Content-Based Filtering, Collaborative Filtering, E-Commerce Recommendation System, Hybrid Techniques.

Yan,K. (2023). A review of techniques used in e-commerce recommendation system. Applied and Computational Engineering,4,629-635.
Export citation

References

[1]. T. Badriyah, E. T. Wijayanto, I. Syarif, and P. Kristalina, “A hybrid recommendation system for E-commerce based on product description and user profile,” in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), 2017: IEEE, pp. 95-100.

[2]. J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, “Collaborative Filtering Recommender Systems,” in The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 291-324.

[3]. H. Li, F. Cai, and Z. Liao, “Content-based filtering recommendation algorithm using HMM,” in 2012 Fourth International Conference on Computational and Information Sciences, 2012: IEEE, pp. 275-277.

[4]. R. Van Meteren and M. Van Someren, “Using content-based filtering for recommendation,” in Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, 2000, vol. 30, pp. 47-56.

[5]. P. Melville and V. Sindhwani, “Recommender systems,” Encyclopedia of machine learning, vol. 1, pp. 829-838, 2010.

[6]. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61-70, 1992.

[7]. J. B. D. CarlKadie, “Empirical analysis of predictive algorithms for collaborative filtering,” Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA, vol. 98052, 1998.

[8]. D. Liu, “A Study on Collaborative Filtering Recommendation Algorithms,” in 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018: IEEE, pp. 2256-2261.

[9]. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285-295.

[10]. G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborative filtering,” IEEE Internet computing, vol. 7, no. 1, pp. 76-80, 2003.

[11]. P. Cotter and B. Smyth, “Ptv: Intelligent personalised tv guides,” in AAAI/IAAI, 2000, pp. 957-964.

[12]. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. M. Sartin, “Combining Content-Based and Collaborative Filters in an Online Newspaper,” in SIGIR 1999, 1999.

[13]. P. Melville, R. J. Mooney, and R. Nagarajan, “Content-boosted collaborative filtering for improved recommendations,” Aaai/iaai, vol. 23, pp. 187-192, 2002.

[14]. Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, “A review on deep learning for recommender systems: challenges and remedies,” Artificial Intelligence Review, vol. 52, no. 1, pp. 1-37, 2019.

[15]. R. Devooght and H. Bersini, “Long and short-term recommendations with recurrent neural networks,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 13-21.

Cite this article

Yan,K. (2023). A review of techniques used in e-commerce recommendation system. Applied and Computational Engineering,4,629-635.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. T. Badriyah, E. T. Wijayanto, I. Syarif, and P. Kristalina, “A hybrid recommendation system for E-commerce based on product description and user profile,” in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), 2017: IEEE, pp. 95-100.

[2]. J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, “Collaborative Filtering Recommender Systems,” in The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 291-324.

[3]. H. Li, F. Cai, and Z. Liao, “Content-based filtering recommendation algorithm using HMM,” in 2012 Fourth International Conference on Computational and Information Sciences, 2012: IEEE, pp. 275-277.

[4]. R. Van Meteren and M. Van Someren, “Using content-based filtering for recommendation,” in Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, 2000, vol. 30, pp. 47-56.

[5]. P. Melville and V. Sindhwani, “Recommender systems,” Encyclopedia of machine learning, vol. 1, pp. 829-838, 2010.

[6]. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61-70, 1992.

[7]. J. B. D. CarlKadie, “Empirical analysis of predictive algorithms for collaborative filtering,” Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA, vol. 98052, 1998.

[8]. D. Liu, “A Study on Collaborative Filtering Recommendation Algorithms,” in 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018: IEEE, pp. 2256-2261.

[9]. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285-295.

[10]. G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborative filtering,” IEEE Internet computing, vol. 7, no. 1, pp. 76-80, 2003.

[11]. P. Cotter and B. Smyth, “Ptv: Intelligent personalised tv guides,” in AAAI/IAAI, 2000, pp. 957-964.

[12]. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. M. Sartin, “Combining Content-Based and Collaborative Filters in an Online Newspaper,” in SIGIR 1999, 1999.

[13]. P. Melville, R. J. Mooney, and R. Nagarajan, “Content-boosted collaborative filtering for improved recommendations,” Aaai/iaai, vol. 23, pp. 187-192, 2002.

[14]. Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, “A review on deep learning for recommender systems: challenges and remedies,” Artificial Intelligence Review, vol. 52, no. 1, pp. 1-37, 2019.

[15]. R. Devooght and H. Bersini, “Long and short-term recommendations with recurrent neural networks,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 13-21.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.