The document outlines a project aimed at supporting local merchants in Pakistan by creating a community platform for users to share reviews, discover local products, and promote small businesses. It details the project's objectives, features of the web and mobile application, and discusses the technologies used, including the MERN stack and TensorFlow for recommendations. Challenges include managing fake reviews and the need for reliable data to train the recommendation system.

1. City Spot
TEAM MEMBERS
2017445 SUBHASH
2017192 HUZAIFA
SUPERVISOR
MR. ALI SHAUKAT
CO-SUPERVISOR
MR. HAMMAD MEHMOOD

2. Problem Statement
The main objective of our project is to advance the local merchants, by creating a community
where people can share their meaningful experiences and help each other find the best options
available out there, saving time and money.
This will help both the consumers and small scale business who can't spend on showcasing.
The pipeline, as of now, for our project would be:
1. A registered user could add a review about a item etc.
2. People could search items near them according to their needs.
3. The system would intelligently recommend nearby products.

3. Motivation
Over the past few years the topic of reputation marketing has really taken off.
Assisting the local merchants by promoting them.
No such platform for local businesses in Pakistan.

4. Existing Systems
City Spot Angie’s List Glassdoor
Deals in Local vendors. Local business and
contractors.
Companies.
Membership Free. Paid subscription. Free.
Guest user Only see reviews/Cannot
post.
No such facility. Need to be signed up.
Anonymity No. No. Yes.

5. https://www.angieslist.com/ https://www.glassdoor.com/

6. Features and end product
Our final product would be web and mobile application.
Our product will allow the user to register/login.
Users could register a new vendor, if already not registered.
Users could write reviews and also view other reviews along with their locations.

7. Continued…
Put vendor into verified list if reviewed multiple times.
System can block multiple reviews from same IP address.
Content based recommendation system which learns on the user's likes and dislikes based on an
item's features and suggests products.

8. Technologies
Our Project will be both web and mobile application.
So we’ll use ‘MERN’ stack for web application and ‘React native’ for mobile application.
We will use React.js as the frontend of web application.Node.js/Express.js for backend.
We will use MongoDB as our common database for both applications.
For recommender system, we will use Python/Tensorflow to build a robust content-based
recommender system.

9. Budget Requirement
The tools/technologies we would be working are open-source and free of cost for development
projects therefore there is no need for a budget.

10. Conclusion: Limitations and future work
Limitations:
 The main challenge of our project is to avoid fake reviews.
 The data needed to train the recommender system would be quite difficult to gather so we need to
make sure we collect the data in time.
 The major constraint would be handle to fake data, and multiple entries of the same vendor.
Future Work:
 Better and efficient ways to handle fake reviews.
 To provide honest opinions to the users.
 A more robust recommendation system. (trained on larger dataset).

11. Thank you and questions?