Open AccessA THOROUGH STUDY ON PRODUCT RECOMMENDATION
Author(s) -
Aniket Tale,
Suraj Patil,
Snehal Lodade,
Pratik Sonawane
Publication year - 2021
Publication title -
international journal of engineering applied science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-2143
DOI - 10.33564/ijeast.2021.v05i10.030
Subject(s) - computer science , recommender system , collaborative filtering , product (mathematics) , data pre processing , preprocessor , the internet , artificial neural network , fuzzy logic , data mining , artificial intelligence , world wide web , data science , machine learning , geometry , mathematics
The improvements in the internetinfrastructure and the increased affordability has led to anincrease in the number of users on this platform. This hasput a large impact on the services being offered on thisplatform especially on the e-commerce websites. Thesewebsites cater to the individual and the increased numberof users has led to an increase in customer data. This datais highly valuable as it can allow for the effectiveprediction of customer behavior. Therefore, thesepredictions can allow for effective and accurate productrecommendations based on the interests and the behaviorof the customer. To achieve this approach, this researcharticle analyzes a collection of related works based on theparadigm of product recommendation. After a thoroughanalysis, an improved product recommendation system isdevised through the effective implementation of NaturalLanguage Processing and machine learning algorithms.The proposed methodology performs preprocessing, Bagof Words, and TF-IDF along with Fuzzy Artificial NeuralNetworks and Collaborative Filtering to achieve aneffective Product Recommendation system. This approachwill be expanded further in the upcoming researches.