Recommendation System for the E-Commerce Businesses

Running head: RECOMMENDATION SYSTEM
Recommendation System for the E-Commerce Businesses
Name of the Student
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Author Note

RECOMMENDATION SYSTEM
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Abstract:
The main aim of this document performing a survey in the aspect of recommendation system and
getting a brief idea regarding why this important for the e-commerce organizations. In this paper
first a brief introduction has been provided regarding recommendation system and its importance
towards the e-commerce organization has been elaborated. The survey report on this paper has
described important aspects of this report. In the following section of this report a brief
discussion has been done in the aspects of working procedure of the recommendation systems.
Business intelligence is very much important for all the organization. Thus in the following
section, the contribution of recommendation system in the business intelligence has been
elaborated. This report has also demonstrated various of methods following which
recommendation system actually works. The three type of methods are the content based
recommendation system, collaborative recommendation system and hybrid recommendation
system.

RECOMMENDATION SYSTEM
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Table of Contents
Introduction:....................................................................................................................................3
Survey on Recommendation System:..............................................................................................4
About this Paper:.........................................................................................................................4
Working Procedures of Recommendation System:.....................................................................5
Recommendation Systems in Business Intelligence:..................................................................8
Various Methods of Recommendation System:..........................................................................9
Conclusion:....................................................................................................................................11
References:....................................................................................................................................13

RECOMMENDATION SYSTEM
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Introduction:
Recommendation system is important for the organizations, especially for the e-
commerce organizations as it helps them in their important operations. The recommendation
system utilizes various of techniques related with data mining so that meaningful suggestion can
be provided to some group of users or individual users regarding elements or products which can
attract them (Covington, Adams and Sargin 2016). The current era is largely dependent on the
internet services on which various of data are available. Due to this large quantity of data in the
web there are several of choices are present in the web and due to this fact perfect filtering is
required in this case. In this aspect there are many approaches to the recommendation system
which has been developed till the date and also interest particularly in this area of data mining is
quite high due growing demand of the practical applications (Melville and Sindhwani 2017). In
this case it can be deal with some personalized recommendation and the overloaded data can be
also dealt with it. By the implementation of information filtering, information can be prioritized
and relevant information can be provided to the users effectively without any type of information
overloading problems. Particularly a user have huge amount of choices from the overloaded
information, but thorough using accurate recommendation system the user will only know about
relevant information based on his/her interest. Thus, for navigating the users as per the choices of
them recommendation system can play an important role.
The first ever recommendation system was developed in 1992 by Goldberg, Nichols, Oki
& Terry. This system was known as Tapestry which was an electronic messaging system. This
system was capable of allowing the users to like or dislike the item. In this aspect a technology
regarding personalized information filtering technology has been used for predicting whether a
specific user will like a specific product or not. In the further cases many more other applications