Recommender System in Big Data Environment
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
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