<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Bucketplace on Jungbae Park</title><link>https://jungbaepark.github.io/blog/tags/bucketplace/</link><description>Recent content in Bucketplace on Jungbae Park</description><generator>Hugo -- 0.152.2</generator><language>en-us</language><lastBuildDate>Thu, 15 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jungbaepark.github.io/blog/tags/bucketplace/index.xml" rel="self" type="application/rss+xml"/><item><title>Agentic Compositional Multimodal Natural Language 3D Model Search</title><link>https://jungbaepark.github.io/blog/projects/agentic-3d-model-search/</link><pubDate>Thu, 15 Jan 2026 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/agentic-3d-model-search/</guid><description>Building an agentic multimodal search system (CoI-Fit) for 3D product retrieval at Bucketplace, combining LLM reasoning with BM25+KNN hybrid search across visual, textual, and spatial signals via LangGraph and A2A protocol.</description></item><item><title>Scene-to-Products Retrieval for Digital Twin (Image-to-3D)</title><link>https://jungbaepark.github.io/blog/projects/digital-twin-scene-retrieval/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/digital-twin-scene-retrieval/</guid><description>Built an end-to-end scene-to-products retrieval pipeline for Digital Twin at Bucketplace, achieving 13x recall improvement, 6,000x cost reduction in 3D collaboration, and scaling the catalog from 30K to 400K+ products.</description></item><item><title>OHouseAI GenAI Workflow Systematization</title><link>https://jungbaepark.github.io/blog/projects/ohouseai-genai-workflow/</link><pubDate>Sat, 15 Nov 2025 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/ohouseai-genai-workflow/</guid><description>Led systematization of OHouseAI&amp;rsquo;s GenAI interior design service at Bucketplace, achieving +253% net satisfaction, 2x latency reduction, and outperforming GPT-IMAGE-1.5 and Gemini Nano. Reached Korea #8 in Graphics/Design.</description></item><item><title>Personalized Multimodal Retrieval with VLM Distillation</title><link>https://jungbaepark.github.io/blog/projects/multimodal-retrieval-vlm/</link><pubDate>Sun, 15 Jun 2025 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/multimodal-retrieval-vlm/</guid><description>Developed personalized multimodal retrieval combining SASRec sequential recommendation with Jina-CLIP-V2 vision-language representations, using knowledge distillation for efficient production serving at Bucketplace.</description></item><item><title>Search OKR: E-commerce Deals / Price 2.0 Ranking Optimization</title><link>https://jungbaepark.github.io/blog/projects/search-okr-ecommerce/</link><pubDate>Sun, 15 Jun 2025 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/search-okr-ecommerce/</guid><description>Delivered measurable search ranking OKR impact at Bucketplace through 4 production A/B experiments (3 winners shipped), achieving +11.17% buyer conversion on category pages and +7.35% deals exposure on store search.</description></item><item><title>Multi-Objective Model Hyperparameter Optimization for E-commerce Search</title><link>https://jungbaepark.github.io/blog/projects/mohper-automl-ecommerce/</link><pubDate>Wed, 15 Jan 2025 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/mohper-automl-ecommerce/</guid><description>Built a multi-objective hyperparameter optimization framework for e-commerce search at Bucketplace, launching tuned parameters 5x+ on SERP and 3x+ on CPLP for CTR/CVR gains. Accepted to CIKM'25 Applied Research Track.</description></item><item><title>Global Content Search Ranking System</title><link>https://jungbaepark.github.io/blog/projects/global-content-search-ranking/</link><pubDate>Fri, 15 Sep 2023 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/global-content-search-ranking/</guid><description>Built a two-stage content ranking system at Bucketplace covering 3 content types, with ElasticSearch function-score reranking, time decay, global language analysis, and Redis-cached serving.</description></item><item><title>CLIP-based E-Commerce Multimodal Search and Classification</title><link>https://jungbaepark.github.io/blog/projects/clip-ecommerce-multimodal/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/clip-ecommerce-multimodal/</guid><description>Shipped CLIP-based multimodal retrieval at Bucketplace, achieving +16.39% query CTCVR, 2x inference speed via quantization, and 92.23% Top-10 accuracy across 2,000+ product categories.</description></item><item><title>Design &amp; Development of a Shared Team ML Mart</title><link>https://jungbaepark.github.io/blog/projects/shared-team-ml-mart/</link><pubDate>Thu, 01 Jun 2023 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/shared-team-ml-mart/</guid><description>Designed a shared ML feature mart for Bucketplace Search Team with client-side log validation, lag anomaly detection, and automated A/B test analysis pipeline using PySpark and HiveDB.</description></item></channel></rss>