Performance analysis of classification and ranking techniques

Recommendation systems aim at recommending relevant items to the users of the system. Recommendation Systems provide efficient recommendations based on algorithms used for classification and ranking. There exist various ways by which classification can be achieved in a supervised or unsupervised manner. Since the sample datasets that are used for experiments are large and also contain more number of feature sets, it is essential to understand dataset beforehand. Also when results are shown to the user, big challenge is how well data can be ranked so that user satisfaction is guaranteed. When data sets are large, some ranking algorithms perform poorly in terms of computation and storage. Thus, these kinds of algorithms are quite expensive. We aim at developing classification and ranking algorithm which will reduce computational cost and dimensionality of data without affecting the diversity of the feature set. Dimensionality of data can be handled by SVM (Support Vector Machine). AUC (Area under the Curve) and WARP (Weighted Approximately Ranked Pairwise) algorithms are efficient for ranking of the items which are of user interest.