Date Received: 20-07-2020 / Date Accepted: 29-03-2020 / Date Published: 03-07-2025
The designed recommender system is a tool to provide important suggestions forusers or customers. Based on the datasets of user relationships, products, andprevious behavior of consumers, smart recommendations for the preferences of eachconsumer are given, which helps consumers tomake good decisions while shopping online. In this article, we present an overview of some methodologies of recommendation systems, techniques of recommender systems, and evaluate the strengths and weaknesses of each technique, as well as comparedsome benefits of recommender systems in e-commerce. Moreover, we report some challenges that the recommender systems are facing and list some solutions to solve these challenges. Our experimental results on the four datasets (Movielens100k, Epinions,BookCrossing, LastFM) showed that there was no best recommendation algorithm in all evaluation metrics. Finally, we built an e-commerce website that integrated some different techniques of recommender systems such as non-personalized methods, personalized methods to recommend the right product for each customer. The experimental system gives some diverse suggestions to overcome the problem "Cold start problem" of personalized methods.
