Vendscon

Vendscon is a multivendor ecommerce website, it refers to the buying and selling of goods or services using the internet, and the transfer of money and data to execute these transactions. By using Vendscon platform one will be able to buy products from multiple vendors of his/her choice. Vendscon al

Project Title

Vendscon

Project Area of Specialization

Artificial Intelligence

Project Summary

Vendscon is a multivendor ecommerce website, it refers to the buying and selling of goods or services using the internet, and the transfer of money and data to execute these transactions. By using Vendscon platform one will be able to buy products from multiple vendors of his/her choice. Vendscon also lets you sell your products and open your own store. It also offers users a suite of services "including payments, marketing, shipping and customer engagement tools to simplify the process of running an online store for small merchants.

The distinct feature that we will have in our application is the RECOMMENDATION ENGINE / ADVICE GENERATION, our main focus will be to develop a top class recommendation engine for ecommerce website

The project scope has been divided into two parts/milestones. The first goal is to develop a complete prototype for the ecommerce application. The second and main

goal of this project is convert the prototype into fully functioned ecommerce website with a top notch recommendation system for the application that will have all the features for the customers and seller to do their all kinds of business activities

Project Objectives

A product recommendation engine (aka reco engine) is a software that tracks the user’s behavior on e-commerce sites and based on that, it suggests products that users might be interested in. The product recommendation can be made directly on the website, within emails or on advertising banners. The more advanced the software, the more accurate are its predictions, thus a high-quality e-commerce recommendation engine can have an important impact on the e-commerce conversion rate.  

Recommendation engines filter and sort your online store’s product offers on the basis of a set of rules. This process uses the data about your products, such as the number of views, sales, or even reviews, to present the most popular and valued products without the customer’s need to search for them. The presentation of these results can be as simple as the order of the products’ appearance on the category page.

User-specific data, on the other hand, such as the customer’s most viewed categories, products, and purchase history,  allow a recommendation engine to find the most relevant offers for your customers. The resulting recommendations are capable of fueling your personalized advertisements, email marketing campaigns, or special offers on the landing and category pages of your website.

Project Implementation Method

There are different types of methods that vendscon ecommerce application recommendation engine will use:

Collaborative filtering algorithms are based on collecting and interpreting large volumes of user behavior data. They also compare similar actions by different users and try to predict what a specific user might be interested in. There are not based on machine-learning but purely on correlating big amounts of data, thus they don’t need to ‘understand’ what the actual interests of the user are. Some of the most famous collaborative recommendation algorithms are used by Amazon, Facebook or LinkedIn. 

Content-based filtering algorithms are based on a user profile and on the description of the items that the user interacts with. This type of algorithm is heavily based on keyword and on storing the actions of users in dedicated profiles, based on which product recommendations will be made. One key feature these algorithms implement is asking the users to vote (up or down) various types of content, thus with each vote fine-tuning the user profile according to their preferences. 

AI-based reco engines are a big part of the future of e-commerce in general. Currently, there are no real AI reco engines but they are being developed by various companies in the field and it won’t be long until they will become publically available. They will be the most complex algorithms since they require the software to constantly learn and update its self, but they will most likely be the most efficient types of reco engines for e-commerce. 

Benefits of the Project

The biggest challenge for e-commerce businesses is ensuring a superior customer service to shoppers. Helping them find what they are looking for and guiding their shopping experience is what makes the process challengeable. In brick-and-mortar stores, you can always find savvy salespeople. They help to find what the shopper looks for and gives specific recommendations based on their preferences and wishes.

The e-commerce stores have chatbots that are supposed to replace the helping salespeople in the brick-and-mortar shops. However, although online salespeople can handle some issues, they are unable to deliver as much value as the person in an actual store. Product recommendation engines were created to overcome the issue of virtual communication in e-commerce stores with the help of personalization.

following are its benefits

• Drive Traffic.
• Deliver Relevant Content.
• Engage Shoppers. 
• Convert Shoppers to Customers. 
• Increase Average Order Value. 
• Increase Number of Items per Order. 
• Control Merchandising and Inventory Rules. 
• Reduce Workload and Overhead.

Technical Details of Final Deliverable

KINDLY VISIT URL FOR TECHNICAL DIAGRAM

Recommender systems are a powerful new technology for extracting additional value for a business from its user databases. These systems help users find items they want to buy from a business. Recommender systems benefit users by enabling them to find items they like. Conversely, they help the business by generating more sales. Recommender systems are rapidly becoming a crucial tool in E-commerce on the Web. Recommender systems are being stressed by the huge volume of user data in existing corporate databases, and will be stressed even more by the increasing volume of user data available on the Web. New technologies are needed that can dramatically improve the scalability of recommender systems.
Technologies to be used

The above approach can be achieved using some of the following technologies

• MEAN/MERN STACK (FRONT END/ BACK END)
• PYTHON/TENSORFLOW ( BIG DATA/ AI/ MACHINE LEARNING)
• .NET CORE
•  MVC ARCHITECTURE

The technologies can be changed on later/ and as for now we have decided to develop the backend using php

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Finance , Security

Core Technology

Artificial Intelligence(AI)

Other Technologies

Cloud Infrastructure, Big Data

Sustainable Development Goals

Good Health and Well-Being for People, Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com