Product recommendation system

Abstract

Recommender systems play a role, in helping consumers by suggesting products. These systems rely on algorithms, many of which are based on machine learning from the intelligence field. However, choosing the algorithm for a recommender system can be pretty challenging due to the range of options available. Additionally developing systems often comes with obstacles. Raises questions.One major challenge in creating a recommendation system for an e-commerce business is the ”cold start” problem. This occurs when there isn’t data in the product dataset. Consequently accurately recommending products to customers becomes extremely difficult. The problem arises because there is no user purchase history or item ratings, for users making it impossible for the system to provide recommendations based on their preferences.In our research, we focus on addressing this cold start problem in recommender systems. Our goal is to find solutions using multiple approaches, including similarity methods and clustering algorithms like Agglomerative Hierarchical and K-means clustering, and also create a merged recommendation system from all the approaches. By doing we aim to overcome the cold start issue and assist businesses in generating accurate recommendations.To conduct our research we use an unsupervised learning dataset since the cold start problem primarily occurs when there is no user data available. Our investigation aims to provide insights and suggestions to address the challenges related to the cold start problem, in recommender systems. This will benefit businesses. Improve the user experience.