Large Scale Follower Recommendation in Instagram

With advancements in predictive and prescriptive analytics over the past few years, various businesses have been extensively using recommender systems for marketing purposes. Say whether it’s some sort of association analysis for customer market or any social networking for connecting people together, this very concept of recommenders has been utilized up to a very large extent over the past decade. When it comes to implementing the underlying concepts behind these systems, most of the time we get to have a paradox of choice of various techniques, which need to be carefully analyzed and acted upon to get the desired accuracy of the model. In cases where the recommendations are made based on a connected graph, we need proper feature generation techniques which play an important role in training the model as per our objective. In this paper, we propose a combination of feature extraction techniques employed over a Gradient Boost Decision Tree model for recommending people whom a person is likely to follow. We also shall investigate a few ways where the recommendation is made based on the common followers of two or more profiles and how likely two or more people may or may not know each other. In any problem, hyperparameter tuning plays the most important part which is a key aspect to optimize the model (so that it doesn’t overfit or underfit the data available) and is totally problem specific.