Multivendor E-Commerce Market Place with Real Time Bidding System and AI Integration
Bibliographic record
Abstract
The evolution of intelligent systems in e-commerce has led to the creation of dynamic platforms that drive user engagement and deliver tailored shopping experiences. This paper presents the design and implementation of a multi-vendor e-commerce marketplace that integrates a real-time bidding (RTB) mechanism enhanced by artificial intelligence (AI). The system aims to provide a competitive yet user-centric environment, allowing vendors to manage product listings and inventories while enabling customers to participate in live bidding sessions for selected products. AI integration enables several advanced functionalities, including predictive analytics for inventory forecasting, fraud detection for transactional integrity, AI-driven customer support via chatbots, and personalized product recommendations. Experimental evaluation confirms the system's effectiveness, showing improved operational efficiency, optimized pricing dynamics, and elevated user interaction metrics. The architecture supports secure data transactions, role-based access control, and real-time updates through WebSocket communication. This study demonstrates the feasibility and impact of combining real-time bidding with AI technologies in a multi-vendor marketplace, laying a strong foundation for the development of intelligent, scalable, and adaptive e-commerce ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".