Friday 29th of March 2024
 

Recommender System in Big Data Environment


Udeh Tochukwu Livinus, Rachid Chelouah and Houcine Senoussi

Recommender systems are an Artificial Intelligence technology that has become an essential part of business for many industries and businesses. Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. The system learns from a customer and recommends products that she will find most valuable from among the available products. They serve many types of E-commerce applications, from direct product recommendation for an individual to helping someone find a gift for a third party. Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this research project, the researcher tries to provide a present an analysis of how recommender systems can be used in E-commerce today, companies, SMEs, and other social institutions to improve sales, maintain good customer relationship and loyalty, and saves customers sourcing time. Recommender Systems too can also be used to analyze Big Data, although there are some key challenges associated with Big Data analytics. The impact of data abundance extends well beyond business. But the computer tools for gleaning knowledge and insights from the Internet eras vast trove of unstructured data are fast gaining ground. At the forefront are the rapidly advancing techniques of artificial intelligence like natural-language processing, pattern recognition and machine learning. The researcher hopes that these key problems with improved recommender systems in the future: hybrid data, predictable recommendations, scalability, and incorporation of content, such problems could be resolved. If recommender systems are able to surmount these challenges, they have the potential to become an essential component of doing business in Ecommerce.

Keywords: Recommender System, SparkR, Collaborative Filtering, K-Means, KNN, Content Based Method, Clustering Hadoop, MapReduce, Million Song Data

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ABOUT THE AUTHORS

Udeh Tochukwu Livinus
Quartz Laboratory, EISTI, Avenue du Parc, Cergy, 95000, France

Rachid Chelouah
Quartz Laboratory, EISTI, Avenue du Parc, Cergy, 95000, France

Houcine Senoussi
Quartz Laboratory, EISTI, Avenue du Parc, Cergy, 95000, France


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