This paper gives an insight of e-commerce and highlights the present scenario of e-commerce in India. It presents the surfing pattern of Indian public to give the critical review on truth of various reports being published from time to time. It also critically analyses the e-commerce with major focus on B2C e-commerce which involves e-tailing. 

In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in ecommerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user's needs and preferences, and UtilityBased Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.

Recommender systems are extensively seen as an effective means to combat information overload, as they redound us both narrow down the number of items to choose. They are seen as assistance us make better decisions at a lower transaction cost. Hence, recommender systems have become omnipresent in e-commerce and are also increasingly used in services in different other domains both online and offline where the number of items exceeds our potentiality to consider them all individually. The research papers recommender systems are software applications or systems that help individual users to discover the most relevant research papers to their needs. These systems use filtering techniques to create recommendations. These techniques are categorized majorly into collaborative-based filtering, content-based technique, and hybrid algorithm. In addition, they assist in decision making by providing product information both personalized and non-personalized, summarizing community opinion, search research papers, and providing community critiques. As a result, recommender systems have been shown to ameliorate the decision.