Challenges and Issues of Recommender System for Big Data Applications

Abstract

AbstractThe knowledge overload is a big problem in today’s world. In reality, the information overload implies the availability of so much data or knowledge that goes beyond the user’s manageable limits and causes a great difficulty in all kinds of decision taking. The main reason we need a recommender system in modern society is that because of the proliferation of the Internet, people have so many options to choose from. A recommender system refers to a system that can predict a user’s future preference for a set of items and recommends the top items. It is a knowledge retrieval application that enhances accessibility as well as efficiently and effectively suggests relevant items to users by considering the user interests and preferences. A recommendation framework tries to tackle the problem of overloading information. There are so many other instances such as these, where we have plenty of data, but we can't decide what we want. Even though volume of information has increased, a new problem has arisen as people have had difficulty selecting the items they actually want to see. Recommenders systems have the ability to change the way websites interact with users and allow businesses to optimize their Return on Investment (ROI); based on the information, they can collect on the preferences and purchases of each customer. A traditional recommendation system cannot do its work without sufficient information, and big data offers plenty of user data such as past transactions, browsing history, and reviews for recommendation systems in order to provide accurate and efficient recommendations. In short, even the most advanced recommenders without big data can’t be successful. This work primarily discusses and reflects on current issues, challenges, and research gaps in the production of high-quality recommender systems. Such problems and challenges will present new paths for study, and the target can be accomplished for high-quality recommender systems. The entire research is broken into major tasks including the study of state-of-the-art approaches for recommender system and big data applications; overcoming the problem of cold start, scalability, and building a proactive recommender system; this research considers the process of development for a generic intelligent recommender system that can be work on more than one domain; it also expands the basic recommender program definition.KeywordsRecommender systemBig dataChallengesCold startScalabilityProactive