A Recommendation System for E-Commerce Products Using Collaborative Filtering Approaches - Neelamadhab Padhy; Sridev Suman; T Sanam Priyadarshini; Subhalaxmi Mallick (Engineering Proceedings, 2024)
The objective of this article is to recommend products using association rule mining from an E-commerce site. This helps us to recommend products through utilizing the filtering concept. In this article, we use the Apriori and FP-Growth algorithms. Our model not only suggests products but also gives tips on how to make strong suggestion systems that can deal with a lot of data and give quick responses. Our objective is to predict ratings so that the users could be recommended and buy products. There are 1,048,100 records in the dataset. This dataset consists of four features, and these are are follows: {user-id, productid, Ratings, and timing}. Here, we consider the rating as our dependent attribute, and others factors are independent features. In this article, we use collaborative filtering algorithms (SVD, SVD+, and ALS) and also item-based filtering techniques (KNNBasic) to recommend products. Apart from these, sssociation rule mining, hybridization of Apriori, and FP-Growth are used. K-means clustering is used to identify anomalies as well as to create a dashboard, using Power BI for data visualization. Apart from these, we have also developed a hybridization algorithm using Apriori and FP-Growth. Among all the recommendation algorithms, SVD outperforms in recommending the product, and the average RMSE and MAE values are 1.31, and 1.04, respectively.