Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF techniques uses the user behavior in form of user item ratings as their information source for prediction. There are major challenges like sparsity of rating matrix and growing nature of data which is faced by CF algorithms. These challenges are been well taken care by Matrix Factorization. In this paper we attempt to present an overview on the role of different MF model to address the challenges of CF algorithms, which can be served as a roadmap for research in this area.Comment: 8 pages, 1 figure in IJAFRC, Vol.1, Issue 12, December 201

Evaluation of Famous Recommender Systems: A Comparative Analysis

In this era of the internet and with the easy availability of data at a very low cost, searching for information is growing at an exponential rate. So, it is now impossible to find the desired information without proper guidance. Here is the need of the recommendation system. The system will recommend which information relevant to the user according to their searching pattern. It will also explore hidden results with a minimal effect. The goal of this paper is to describe the famous recommendation systems which are used mostly and to explore what is the need of these kinds of systems and also what kind of technology has been used to provide better services to its users. Finally we would like to show how one recommendation system is different from another as per user need

Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space