Overview

At Bucketplace (오늘의집), shipped multimodal retrieval improvements and quantization optimizations for e-commerce search, along with product classification across a large category space.

Key Achievements

  • Query CTR: +3.03% improvement
  • Query CTCVR: +16.39% improvement
  • Inference Speed: 2x speed improvement via quantization
  • Disk Usage: 1/4 disk reduction through quantization
  • Classification: Top-10 Accuracy 92.23% among >2,000 product categories

Technical Approach

Multimodal Dense Retrieval

Integrated CLIP-based dense vector retrieval into the e-commerce search pipeline, enabling semantic matching beyond traditional BM25 keyword search:

  • Image-to-Text Retrieval: Users can search products using text queries that match against product image embeddings
  • Partial Hybrid Search: Combined dense image retrieval via CLIP with traditional text-based BM25, creating a hybrid retrieval system
  • Query Embedding Service: Built a dedicated service for real-time query vector generation

Production Optimization

  • INT8 Quantization: Achieved 2x inference speed and 1/4 disk usage with minimal quality degradation
  • Serving Architecture: Deployed via Triton Inference Server for efficient batch inference

Product Classification

Built a multi-label classification system across >2,000 product categories:

  • Top-10 Accuracy: 92.23% — enabling automated category assignment at scale
  • Application: Used for product catalog enrichment and search filter generation

Related Works: NAVER Shopping and e-CLIP Architectures

Tech Stack

  • Model: CLIP (OpenAI), SigLIP
  • Optimization: INT8 Quantization, ONNX Runtime
  • Retrieval: Dense Vector Retrieval, Hybrid Search (BM25 + KNN)
  • Serving: Triton Inference Server
  • Search: ElasticSearch

Period

January 2023 - June 2023 | Bucketplace (오늘의집)