Development of Product Recommendation Engine By Collaborative Filtering and Association Rule Mining Using Machine Learning Algorithms

Abstract

Recommendation engines are a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ that user would give to an item. It finds information designs in the informational index by learning customers decisions and produces the results that co-identifies with their requirements. Real time examples like Amazon, have been using a recommendation engine for suggesting the goods or products that customers might also like. As the database used in this paper consists of large amount of data, it becomes a difficult and cumbersome process to provide viable choice of products for all the customers. The need of state of art recommendation engine is a necessity in real world e-commerce platforms to solve the issue and fulfil the customers’ needs. There are numerous ways such as collaborative filtering, content-based filtering, hybrid filtering, etc. to build a recommendation system. This paper developed a product recommendation engine that uses collaborative filtering approach, which finds similarity between items bought by the customers with other customers, purchase pattern, and association rule mining framework. The recommendations were generated in order to facilitate ‘cross-sell’ across various items. The collaborative filtering (CF) approach produced the top 10 recommendations for each user. The association rule mining produced the rules based on minimum support (at 0.001) and minimum confidence (at 0.8) values. These values produced around 40,000 viable rules. It can be inferred that selection of metrics and the computation speed is important for quality recommendations.