Top Challenges from the first Practical Online Controlled Experiments Summit - Meta Research | Meta Research
Top Challenges from the first Practical Online Controlled Experiments SummitSIGKDD Explorations Online controlled experiments (OCEs), also known as A/B tests, have become ubiquitous in evaluating the impact of changes made to software products and services. While the concept of online controlled experiments is simple, there are many practical challenges in running OCEs at scale and encourage furth
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KDD2021è«–æ–‡èªã¿ä¼š(Zoom視è´ã‚‚ã‚ã‚Š) (2021/08/28 14:00〜)
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All You Need to Know to Build a Product Knowledge Graph (KDD 2021 Tutorial)
All You Need to Know to Build a Product Knowledge Graph (KDD 2021 Tutorial) Key Questions the Tutorial Answers: What are unique challenges to build a product knowledge graph and what are solutions? Are these techniques applicable to building other domain knowledge graphs? What are practical tips to make this to production? Slides: Overview and Introduction (pdf) Knowledge Extraction (pdf) Knowledg
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ã“ã‚“ã«ã¡ã¯ã€‚ZOZOç ”ç©¶æ‰€ã®å±±ï¨‘ã§ã™ã€‚ ZOZOç ”ç©¶æ‰€ã§ã¯ã€æ¤œç´¢/推薦技術をメインテーマã¨ã—ãŸè«–æ–‡èªã¿ä¼šã‚’進ã‚ã¦ãã¾ã—ãŸã€‚週ã«1回ã®é »åº¦ã§ç™ºè¡¨æ‹…当者ãŒèªã‚“ã§ããŸè«–æ–‡ã®å†…容を共有ã—ã€ãã®å†…容をå‚åŠ è€…ã§è°è«–ã—ã¾ã™ã€‚ 本記事ã§ã¯ã€ãã®ä¼šã§ç™ºè¡¨ã•ã‚ŒãŸè«–æ–‡ã®ã‚µãƒžãƒªãƒ¼ã‚’紹介ã—ã¾ã™ã€‚ 目次 目次 検索/推薦技術ã«é–¢ã™ã‚‹è«–æ–‡èªã¿ä¼š 発表論文ã¨ãã®æ¦‚è¦ SIGIR [SIGIR 2005] Relevance Weighting for Query Independent Evidence [SIGIR 2010] Temporal Diversity in Recommender System [SIGIR 2017] On Application of Learning to Rank for E-Commerce Search [SIGIR 2018] Should I Follow the Crow
Learning a unified embedding for visual search at Pinterest | the morning paper
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic Learning a unified embedding for visual search at Pinterest Zhai et al., KDD’19 Last time out we looked at some great lessons from Airbnb as they introduced deep learning into their search system. Today’s paper choice highlights an organisation that has been deploying multiple dee
Learning a Unified Embedding for Visual Search at Pinterest
At Pinterest, we utilize image embeddings throughout our search and recommendation systems to help our users navigate through visual content by powering experiences like browsing of related content and searching for exact products for shopping. In this work we describe a multi-task deep metric learning system to learn a single unified image embedding which can be used to power our multiple visual
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce 概è¦In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition
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https://dl.acm.org/doi/abs/10.1145/3292500.3330671
Applying NLP for building domain ontology: fashion collection
GitHub - heathersherry/Knowledge-Graph-Tutorials-and-Papers: Insightful Tutorials and Papers about Knowledge Graphs
KDD 2018 Tutorial T39: Building a Large-scale, Accurate and Fresh Knowledge Graph
Building a Large-scale, Accurate and Fresh Knowledge Graph
International Workshop on Knowledge Graph: Heterogeneous Graph Deep Learning and Applications
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Web-Scale Responsive Visual Search at Bing
Visual Search at Alibaba
Amazon@KDD
Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs
(PDF) Beyond Clothing Ontologies: Modeling Fashion with Subjective Influence Networks
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