Recommendation based ordering system using graph database

Project Title

Recommendation based ordering system using graph database

Project Area of Specialization Internet of ThingsProject Summary

During the last decades, the online retailing business has globally been experiencing a substantial growth. Indeed, according to the results of a study executed by the Centre of Retail Research, this sector experienced a growth rate of 18.6% in Europe in 2015 and 16.7% in 2016, making it the fastest growing retail market in that region. The increasing competition on the online retailing market has forced these businesses to improve their techniques and their online environment to target their customers accurately and to maintain a high level of satisfaction. While the number of products available online is excessively large, online retailers now aim at instantly providing their customers with the products they are seeking, reducing the efforts of their customers as much as possible. Consequently, the importance of product recommender systems has become obvious and clear for the leading companies in the online retailing market. Recommendation systems have become serious business tools and are re-shaping the world of e-commerce. Effective recommendations are a valuable service to the customers and a profitable service to the retailer. According to Huseynov, Huseynov & Özkan (2016), recommender systems can commonly be described as “intelligent software providing easily accessible, high-quality recommendations for online consumers” [1]. Such systems are now considered serious business tools helping customers to find the item they are seeking or suggesting them additional ones.

Project Objectives

• To increase efficiency and improve services provided to the customers through better application of technology in daily operations.
• To be able to stand out from competitors

• To make an online ordering system for the organization
• To reach more customers
• To provide real-time recommendations to customers
• To keep record of each customer, product and order

Project Implementation Method

We’ll complete this project using waterfall model. First we’ll collect requirements, then we’ll analyze these requirement. After analysis we’ll model the design. Then we’ll develop a system from this model using Asp.net and Neo4j. We’ll use collaborative and content based filtering using cypher query language of graph database for recommendations. Then the system will be tested and deployed.

Benefits of the Project

Numerous advantages are found in the use of accurate recommender systems. In fact, previous research has established that the use of accurate product recommender systems helps online customers make better decisions during their purchases, reducing the time and efforts put in their search. The use of such system can increase the number of purchases. Often cited as examples, Amazon.com, the largest online retailer in the world and Netflix.com, the largest online distributor of streaming media, are two well-known and successful websites that established efficient product recommenders.

Technical Details of Final Deliverable

The use of such system can increase the number of purchases. Often cited as examples, Amazon.com, the largest online retailer in the world and Netflix.com, the largest online distributor of streaming media, are two well-known and successful websites that established efficient product recommenders.

Final Deliverable of the Project Software SystemType of Industry Others Technologies OthersSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	10000
domain registration,data colletion	Miscellaneous	1	10000	10000