Product Recommendation System Powered by Artificial Intelligence
Keywords:
Recommendation, black-box, Amazon, Netflix, characteristics, consumersAbstract
The recommendation system is a mysterious tool that analyses a group of users and suggests products they might like. The main purpose of the recommender system is to generate useful recommendations to a bundle of users or items or products that might attract them. Recommendation for books on Amazon, or movies on Netflix, or some products on other web sites are real-world examples of functions of industry-strength recommender systems. Such a recommendation system's design is influenced by the platform and specific properties of the available data. The goal of the recommender system is to help consumers or users and to learn about new products and desirable ones among their choices.
References
Kordík P. (2018 Jun 4). Machine Learning for Recommender systems—Part 1 (algorithms, evaluation and cold start). [Online]. Recombee Blog.
Zhang Y. (2020 Aug 5). Machine Learning for Building Recommender System in Python. [Online]. Medium. Towards Data Science. Available from: https://towardsdatascience.com/machine-learning-for-building-recommender-system-in-python-9e4922dd7e97
Wikipedia Contributors. (2023). Recommender system. [Online]. Wikipedia, Wikimedia Foundation. Available from: https://en.wikipedia.org/wiki/Recommender_system
Maruti Techlabs. (2017 Oct 5). 5 Advantages Recommendation Engines can Offer to Businesses. [Online]. Medium, Towards Data Science. Available from: https://towardsdatascience.com/5-advantages-recommendation-engines-can-offer-to-businesses-10b663977673
Bhangale PP, Patil PY, Patil PD. Improved crop yield prediction using neural network. Int J Adv Res Innov Ideas Educ (IJARIIE). 2017; 3(2): 3094–101.
Khare A, Jayakumar N. Perspective Analysis Recommendation System in Machine Learning. Int J Emerg Trends Technol Comput Sci (IJETTCS). 2017; 6(2): 184–7.
Renjith S, Anjali C. A personalized travel recommender model based on content-based prediction and collaborative recommendation. Int J Comput Sci Mob Computing, ICMIC13. 2013 Dec; 66–73.
Atul Kumar, Sumit Kaur, et al. Heuristic Approach for Recommending Research Articles Using Collaborative Tagging. Int J Innov Eng Technol (IJIET). 2015; 5(2): 97–103.
Yaslan Y, Gülaçar H, Koç MN. User Profile Analysis Using an Online Social Network Integrated Quiz Game. Süleyman Demirel Üniv Fen Bilim Enst Derg. 2017; 21(3): 696–702.
Bhemwala Vishal H, Modi Jayesh N. A Study of Effectiveness of Collaborative Filter in Web Mining. Int J Res All Subj Multi Lang. 2020; 8(7): 6–12.
Downloads
Published
Issue
Section
License
Declaration and Copyright Transfer Form
(to be completed by authors)
I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.
- I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
- I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
- I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
- I/We also agree to the authorship of the article in the following order:
Author’s name
1. ________________
2. ________________
3. ________________
4. ________________
| We Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal. |
