Design and Development of a Novel Approach for Recommendation of PG Accommodation System

Abstract

With the growing migration of students and professionals to urban areas, the demand for temporary housing such as paying guest (PG) accommodations has risen sharply. Traditional methods of locating PGs are often inefficient, time consuming, and lack personalization. This paper presents a novel web-based recommendation system tailored for PG accommodations, integrating location aware searches, secure booking, and a recommendation engine based on user preferences and feedback. The system streamlines the interaction between tenants and property owners, ensuring transparency, trust, and accessibility. We highlight the system\'s architecture, functional components, and test results, emphasizing its effectiveness through comparative evaluation and performance analysis. The approach offers a scalable and user centric solution, redefining the PG rental experience in urban environments. The suggested approach, in contrast to generic accommodation platforms, tackles the particular difficulties that come with PG housing, including secure communication, flexible tenancy, community-based feedback, and listing verification. The system\'s responsive design architecture, real-time booking updates, and modular recommendation engine not only increase usability but also guarantee greater tenant satisfaction. The platform\'s performance under concurrent loads has been thoroughly tested, and its hybrid filtering approach for tailored recommendations is what makes it novel. The goal of this study is to close the technological divide in the quest for affordable housing, particularly for students and professionals in their early careers.

Introduction

1. Introduction

Rapid urbanization has increased demand for flexible and affordable PG housing, especially among students and professionals. Existing methods for finding PGs are broker-dependent, time-consuming, and unreliable.

2. Problem & Objective

Although digital platforms exist, most lack:

• Personalized recommendations

• Real-time availability

• Secure communication

• Verified listings

Objective: To create a smart, user-centric web platform that streamlines PG discovery, booking, and communication using AI-driven recommendations, verified listings, and secure transactions.

3. Key Features of the Proposed System

• Personalized Recommendation Engine: Uses user preferences, behavior, and ratings to suggest PGs.

• Verified Listings: Only authenticated owners can list properties.

• Real-Time Filters: Users can search based on budget, location, gender, and amenities with live availability.

• Secure Payments: Integrated with Razorpay for transparent transactions.

• Direct Messaging: Tenants can message PG owners directly, eliminating intermediaries.

• Cross-Platform Support: Works across desktops, tablets, and smartphones.

4. Technical Stack

• Frontend: HTML, CSS, JavaScript, Bootstrap, EJS

• Backend: Node.js, Express.js

• Database: MongoDB

• Third-Party APIs: Google Maps (location), Razorpay (payments), Twilio/SendGrid (notifications)

5. Methodology

• Requirement Gathering: Surveys and interviews with students, professionals, and PG owners.

• Architecture: Multi-tier system with separate presentation, logic, and data layers.

• Development Approach: Agile, modular, and user-centric.

6. Recommendation Engine

Uses:

• User preferences (location, price, amenities)

• Past searches and bookings

• User ratings and feedback

• Popularity metrics (booking frequency)

7. Testing & Evaluation

Conducted thorough testing:

• Unit Testing: Each module (login, search, payment)

• System Testing: Full platform functionality

• Performance Testing: Load simulation with JMeter

• User Testing: Feedback on usability and clarity

8. Novel Contributions

• Hybrid Recommendation Model (preference + behavior-based)

• Verified Listings & Private Messaging

• Live PG Availability

• End-to-End Booking and Payment Flow

• Community Feedback & Review System

9. Related Work Review

Other platforms like Airbnb, Oyo Life, and Zolo focus on premium or travel accommodations. This system targets affordable, student/professional-focused PG housing, addressing issues such as lack of trust, real-time info, and personalization.

10. Statistical Insights

• 11M+ Indian students migrate for education yearly.

• 62% prefer PGs over hostels.

• 40% express dissatisfaction due to misleading listings.

• User survey (50 participants) revealed:

  • 28% prioritized Smart Search & Filtering

  • 22% preferred Recommendation System

  • 20% valued Booking & Payments

  • 15% wanted Chat with PG Owners

  • 10% highlighted Verified Listings

11. System Uptime

Maintained 99.7%+ uptime over three months, surpassing industry norms for SaaS platforms.

12. Results & Benefits

• For Students: Fast, reliable, and personalized PG search with secure booking.

• For PG Owners: Easy listing, better reach, direct communication with tenants.

• For Platform: Trustworthy, scalable, and data-driven accommodation ecosystem.

Conclusion

The research presented in this paper outlines the successful design and implementation of a web-based Paying Guest (PG) Accommodation System that directly addresses the core issues faced by students and young professionals in urban housing markets. Traditional PG discovery methods are often inefficient, relying heavily on brokers, outdated listings, and non-transparent communication. This system effectively resolves these challenges by offering a digital platform that combines smart recommendations, real-time availability, verified listings, and secure transactions, thereby redefining the PG rental experience. At the core of this solution is a hybrid recommendation engine that utilizes user preferences, browsing behavior, and ratings to provide personalized accommodation suggestions. This ensures that users receive relevant and filtered results tailored to their needs, such as proximity to educational institutions, amenities like Wi-Fi and meals, and specific rent ranges. The inclusion of dynamic filters and live availability prevents users from encountering outdated listings—a common problem in current PG platforms. The platform also integrates secure booking and payment gateways like Razorpay, alongside private in-app messaging, allowing direct communication between PG owners and tenants. Verified listings and review mechanisms further add to the platform’s trust and transparency. From registration to booking confirmation, each step has been tested and validated to ensure smooth functionality and a user-friendly experience. Through user surveys and testing, the system has proven to be efficient, scalable, and highly relevant to its intended audience. Features like Smart Search, Recommendation System, and Secure Booking were identified as top priorities by users, all of which have been addressed in the final design. This system bridges a critical gap in urban housing by offering a tailored, technology-driven solution for PG accommodations. It stands as a scalable model with the potential to significantly improve the experience of finding and managing temporary housing in metropolitan regions.

References

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Copyright

Copyright © 2025 Sakshi Devidas Nikumbhe, Manav Sagar Jadhav, Pratik Digambar Patil, Darshan Mahesh Sonawane, Dr. Umesh B. Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.