1. Introduction

The rapid growth of higher education in India has resulted in millions of graduates entering the job market every year. However, employment opportunities remain unevenly distributed, particularly for students from Tier-2 and Tier-3 institutions, where campus placement opportunities are limited and industry exposure is comparatively low. Traditional placement cells often rely on a restricted network of recruiters, making it difficult for students to discover suitable career opportunities. At the same time, private training institutes compete aggressively to attract students through marketing campaigns, while existing online career platforms frequently combine recruitment services with their own commercial training programs. This creates an inherent conflict of interest, where institute visibility and rankings may be influenced by commercial objectives rather than actual performance. Current employment platforms primarily focus on job listings, internships, or professional networking, but they provide limited transparency regarding institute quality and ranking methodologies. In many cases, ranking algorithms are proprietary and cannot be independently verified, making it difficult for students to distinguish merit-based recommendations from sponsored visibility. Such limitations reduce trust in the recruitment ecosystem and create challenges for educational institutes seeking fair representation based on their placement performance and student outcomes. To address these challenges, this paper presents Road2Job, a campus-focused career platform designed specifically for students, employers, and educational institutes. Unlike conventional recruitment portals, Road2Job separates merit-based institute ranking from promotional activities. Institute rankings are computed using transparent and measurable parameters, including placement quality, profile completeness, update consistency, and recent activity, while paid promotional campaigns influence only sponsored content visibility without affecting ranking scores. This separation ensures fairness, transparency, and credibility within the platform. The platform further incorporates a skill-based job matching mechanism that evaluates candidate suitability using explicitly extracted skills rather than speculative predictions. If sufficient information is unavailable, the system deliberately avoids generating misleading scores, thereby following a zero-fabrication design philosophy. Additionally, Road2Job provides an append-only application tracking mechanism, lightweight real-time messaging between employers and students, and a promotion-driven monetization model that aligns business sustainability with user trust rather than compromising ranking integrity. The primary objective of this work is to demonstrate that a transparent, merit-oriented, and trust-centric campus recruitment platform can effectively connect students, employers, and institutes while maintaining fairness in recommendation and ranking mechanisms. By combining transparent scoring algorithms, explainable ranking metrics, auditable application tracking, and ethical monetization, Road2Job aims to establish a reliable digital ecosystem that improves campus recruitment outcomes and enhances confidence among all stakeholders.

2. Methodology

4.1 Proposed System Road2Job is developed as a campus-focused career platform using a Core PHP 8 Model–View–Controller (MVC) architecture. The platform connects four primary stakeholders: Students, Employers, Educational Institutes, and Administrators. The system is designed with a transparent and merit-based approach, ensuring that institute rankings are generated from measurable performance indicators instead of paid promotions. The application follows a server-rendered architecture with MySQL as the backend database, while JavaScript is used only for progressive enhancement to improve user interaction. 4.2 System Architecture The platform follows a layered request-processing architecture. Every client request is first received by a front controller, which initializes configuration files, user sessions, and the project autoloader. The request is then forwarded to the routing module, where authentication and role-based authorization are performed before invoking the appropriate controller. The controller validates user inputs and forwards the request either to the Model layer for database operations or to the Service layer for analytical computations such as institute ranking, job matching, profile completeness evaluation, and campaign management. Finally, the processed information is rendered through server-side PHP templates and delivered to the user. This modular architecture improves maintainability, scalability, and independent testing of business logic. 4.3 Institute Ranking Method A transparent ranking mechanism is implemented to evaluate educational institutes. The overall ranking score is calculated using four measurable parameters: Placement Quality Update Activity Profile Completeness Activity Consistency These parameters are combined using weighted coefficients, while a spam penalty multiplier reduces the score of institutes exhibiting excessive promotional activity. RankScore=(0.35P+0.25A+0.20C+0.20S)×M where: P = Placement Quality A = Update Activity C = Profile Completeness S = Activity Consistency M = Spam Penalty Multiplier Placement records are de-duplicated and weighted according to recency using exponential decay. Update activity is normalized within a defined observation window, profile completeness is measured using available institutional information, and consistency rewards sustained activity over time instead of short bursts. The spam multiplier penalizes institutes that post an unusually large number of updates within a short duration, thereby reducing opportunities for manipulation. 4.4 Job Matching Algorithm The job recommendation module compares employer requirements with student skills. Required skills are extracted from job descriptions using a predefined skill dictionary after text normalization. The matching percentage is computed as: Match= ∣R∣ ∣R∩D∣ ​ ×100 where: R represents the extracted skills from the job description. D represents the student's declared skill set. If no valid skills can be extracted from a job description, the system intentionally avoids generating a matching score. This zero-fabrication policy prevents misleading recommendations and improves user trust. 4.5 Messaging Module Road2Job implements a lightweight real-time communication mechanism between students and employers. Conversations begin only after a request has been accepted by both parties. Instead of maintaining persistent WebSocket connections, the system periodically polls the server for newly available messages. A temporary typing indicator is generated using timestamp validation and automatically expires after a predefined interval. This approach minimizes infrastructure requirements while remaining compatible with shared PHP hosting environments. 4.6 Application Tracking Every job application progresses through multiple recruitment stages such as Applied, Shortlisted, Interview Scheduled, Selected, or Rejected. Instead of updating a single status field, each transition is recorded as a separate immutable event within an append-only event log. This approach provides a complete audit trail of recruitment activities and allows both students and employers to track application history with accurate timestamps. 4.7 Campaign Management Educational institutes can purchase promotional campaigns to increase the visibility of their updates. Campaign requests include payment verification and administrator approval before activation. To preserve ranking fairness, promotional campaigns influence only the display order within the sponsored updates feed. The ranking algorithm never accesses campaign information, ensuring that institute rankings remain completely independent of financial transactions. 4.8 Experimental Evaluation Since the platform has not yet been deployed at production scale, evaluation is conducted using controlled functional testing and constructed datasets. The experiments validate: Institute ranking behaviour under different scenarios. Job matching accuracy. Messaging functionality. Campaign isolation from ranking scores. Application event tracking. Overall response correctness. The evaluation focuses on functional correctness and behavioural verification rather than large-scale statistical analysis.

3. Results

The proposed Road2Job platform was evaluated using functional testing and constructed datasets to verify the correctness of its major components. Experimental evaluation demonstrated that the institute ranking algorithm successfully prioritized placement performance over promotional activity while effectively penalizing spam posting behavior. The skill-based job matching module generated accurate match percentages only when valid skills were extracted, avoiding misleading recommendations through its zero-fabrication approach. The lightweight messaging system provided reliable communication with low response latency and automatic typing indicator expiry. Application tracking maintained a complete audit trail using an append-only event log, enabling transparent monitoring of every recruitment stage. Furthermore, promotional campaigns increased institute visibility without influencing ranking scores, confirming the successful isolation between monetization and merit-based ranking. Overall, the platform achieved reliable functionality, transparent decision-making, and trustworthy recruitment services while maintaining efficient performance on a standard PHP-MySQL hosting environment.

4. Conclusion

Road2Job presents a transparent and merit-based campus recruitment platform that effectively connects students, employers, and educational institutes through a fair and trustworthy ecosystem. The proposed institute ranking algorithm combines placement quality, activity, profile completeness, and consistency while ensuring that promotional campaigns do not influence ranking outcomes. The skill-based job matching mechanism follows a zero-fabrication approach by displaying recommendations only when sufficient information is available, thereby improving user trust. Additional features such as append-only application tracking, lightweight real-time messaging, and transparent campaign management further enhance the reliability of the platform. The experimental evaluation demonstrates that the proposed architecture successfully maintains fairness, transparency, and functional correctness while remaining suitable for deployment on standard PHP-MySQL hosting environments. Future enhancements include payment gateway integration, empirical tuning of ranking parameters using production data, scalability improvements, and the development of a native mobile application to further strengthen the platform's capabilities.

References

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