Recommender system designed using an ensemble approach to semi-supervised learning

In traditional machine learning approaches only the labeled data are used for training the model but labeled data isnot easily available and also the manual labeling of data requires a considerable amount of time, effort and expense. This is the reason why most of the research remains untouched and this problem is addressed by a new technique called semi-supervised learning. Semi-supervised learning limits the use of labeled data by efficiently using an unlabeled data, which is relatively easily available as compared to labeled data. In this paper, our idea is to use the semi-supervised learning technique in the field of regression. We have proposed a model to predict the label for unlabeled data with few labeled data using an ensemble approach to semi-supervised learning. Furthermore, we have also made a comparison of the result obtained when we use a model with and without semi supervised learning and have also shown how semi supervised learning increases the predictive performance