Item-Based Collaborative Filtering and Association Rules for a Baseline Recommender in E-Commerce

In the ever-growing data-driven world today, data increases in many forms, e.g. e-commerce sites uploading new products, streaming services adding TV shows and movies, and music platforms uploading new songs. It would be highly infeasible for end users to quickly browse all this data. Hence recommender systems can benefit end users (individuals as well as companies) in efficiently finding suitable products. Rather than making end users search through a vast array of items, recommender systems can suggest suitable items to users based on popularity of the items and the respective users' buying behavior. Accordingly, in this paper we explore two techniques widespread in recommender systems, i.e. item-based collaborative filtering and association rule mining, over Amazon review data on cellphones and accessories, and build a baseline recommender system scalable to larger data. Association rule mining is explored using the Apriori algorithm to find patterns in the data from transaction history. Item-based collaborative filtering is deployed using a correlation matrix to find similar products. Both these techniques yield useful results as evident from our baseline experiments. This work constitutes an exploratory study with longtime products in e-commerce and sets the stage for mining online data on relatively new products pertinent to the Covid-19 pandemic. These include face masks, hand sanitizers, disinfectant sprays, antibacterial wipes etc. Since multiple vendors are designing such crucial products today, it is important to provide recommendations to potential buyers. An ultimate goal in our work is to build a recommender app for e-commerce based on interesting results from our findings. This work constitutes intelligent data mining scalable over big data in e-commerce. It makes broader impacts on smart cities, since this fits the smart living and smart economy characteristics.