# eBay's Price Prediction and Similar Item Search System ## Background and Problem Statement eBay, one of the world's largest e-commerce platforms, faces the challenge of helping sellers accurately price their items. With approximately: - 2 billion listings - 130 million active buyers - 190 different sites worldwide The platform needed to develop a system that could assist sellers in pricing their items accurately while maintaining user trust through transparency and similarity-based recommendations. ## Technical Architecture Overview ### Data Processing Pipeline - Uses seller listing creation data - Processes item titles and metadata - Incorporates historical transaction data - Utilizes user search and click behavior data ### Model Development Approaches The team explored three main approaches: ### 1. Semantic Similarity Model - Based on BERT-like transformer architecture - Focused on generating embeddings for item titles - Training data creation: ### 2. Direct Price Prediction Model - Also uses transformer architecture - Directly predicts item price from title - Extracts embeddings from final layer for similarity search - Shows better price accuracy but sometimes lacks semantic relevance ### 3. Multi-Task Hybrid Approach - Combines semantic similarity and price prediction - Uses shared weights between tasks - Allows control over trade-off between price accuracy and similarity - Implements alternative learning between tasks - Uses an alpha parameter to balance between objectives ## Training Implementation Details ### Training Data Generation - Utilized user search and click behavior - Incorporated historical sales data - Added structural metadata validation - Created hard negative examples for better training ### Hard Negative Mining - Specifically selected challenging negative examples - Used items from same category but different price points - Maintained same player/team but different conditions or grades - Helped model learn subtle price-impacting features ## Evaluation and Results ### Metrics - Mean Absolute Error for price prediction - Semantic accuracy for player matching - Combined metrics for overall system performance ### Trade-offs Observed - Price accuracy vs semantic similarity - User trust vs pure price optimization - Model complexity vs interpretability ## Production Implementation ### System Components - Embedding generation pipeline - K-nearest neighbor search system - Price aggregation module - User interface for showing similar items ### Practical Considerations - Balance between price accuracy and item similarity - Need for transparency in recommendations - Importance of user trust in pricing suggestions ## Domain-Specific Challenges ### Sports Trading Cards Use Case - Complex pricing factors: - Need to handle abbreviations and domain-specific terminology - Importance of exact matching for certain attributes ### Text Processing Challenges - Handling abbreviations (RC for Rookie Card) - Processing specialized terminology - Managing multiple formats for same concept - Dealing with seller-specific variations ## Results and Impact ### System Benefits - More accurate price recommendations - Better similar item matching - Increased user trust through transparency - Improved seller experience ### Lessons Learned - Importance of domain-specific training - Value of hybrid approaches - Need for balance between different objectives - Significance of hard negative examples in training ## Future Directions ### Potential Improvements - Further refinement of multi-task learning - Enhanced negative example selection - More sophisticated price aggregation - Extended metadata incorporation ## Technical Implementation Details ### Model Architecture - Based on BERT-style transformers - Modified for multi-task learning - Customized for e-commerce domain - Optimized for both embedding generation and price prediction ### Infrastructure - GPU-based training system - Database storage for embeddings - Real-time inference capabilities - Integration with existing e-commerce platform