Various Recommender Systems and Their usage in the Domains of Mobile Based and Online Applications

Abstract

The activities of the users are surrounded by online shopping, online content fetching and online payment of the various bills. The important point is in case of the online shopping with Amazon, Flipkart and many other online shopping sites provides some sort of intelligent assistance to the user. Based on the past history, based on the user profile. Such kind of the applications were named as recommender systems. The most common categories of recommender systems involves, the collaborative filtering, content-based filtering, multi-criteria recommender systems, risk-aware recommender systems, mobile recommender systems and Hybrid recommender systems. The current work dealing with the process followed by the recommender systems along with various key factors embedded in the usage. The suggestions given by the application to the user depends on user profile, and content searched by the user and the collaboration of other products with the current product. The work focus on the implementation algorithms existing in the process of recommender systems, the above listed categories of recommender systems follow certain key mechanisms depending on the user query. The work also deals with the performance aspects of the recommender systems in case of accuracy and reproducibility in recommender system research. Especially the mobile recommender systems there are certain limitations of region and accuracy of the results. Overall the outcome of the work is to describe the importance of the recommender systems and the internal mechanism followed by various recommender systems.