Recommendation system for Twitch Social Network graph using Link Prediction Technique

Various social networks have become important part of the people. This social network can be represented as graph. which change with respect to time different nodes and edges are added. In this graph users can be represented as nodes and edges can be thought as relationship between these nodes (User). The goal of link prediction is to finding missing links and prediction future relationships. The task of recommending new relationships(edges) to users(nodes) can be framed as task to Link prediction in a graph. We will use supervised Machine learning approaches with a set of manually extracted features along with node embedding generated using node2vec to increase the performance. Then the trained model is used for performing link Prediction between nodes on the Twitch dataset, which is collected from SNAP. recommendation problem can be mapped to a binary classification problem wherein the two classes are - recommend or do not recommend. We propose solving this by generating link predictions and then using the predicted links to recommend streamers. Thus, we aim to train classification models using machine learning to predict whether a link exists between any given pair of nodes and use this prediction for recommendations. of game. The performance of the model is assessed utilizing prediction performance metric