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150 successful Machine Learning models: 6 lessons learned at Booking.com Booking.com is the world’s largest online travel agent where millions of guests find their accommodation and millions of accommodation providers list their properties including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years we have applied Machine Learning to improve the experience of ou
Rainer Schlosser (Hasso Plattner Institute); Martin Boissier (Hasso Plattner Institute) Most online markets are characterized by competitive settings and limited demand information. Due to the complexity of such markets, efficient pricing strategies are hard to derive. We analyze stochastic dynamic pricing models in competitive markets with multiple offer dimensions, such as price, quality, and ra
Mihajlo Grbovic (Airbnb); Haibin Cheng (Airbnb) Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ra
Peng Ye (Airbnb); Julian Qian (Ant financial); Jieying Chen (Airbnb); Chen-Hung Wu (Airbnb); Yitong Zhou (Airbnb); Spencer De Mars (Airbnb); Frank Yang (Airbnb); Li Zhang (Airbnb) This paper describes the pricing strategy model deployed at Airbnb, an online marketplace for sharing home and experience. The goal of price optimization is to help hosts who share their homes on Airbnb set the optimal p
Kui Zhao (Machine Intelligence Technologies, Alibaba Group); Yuechuan Li (Machine Intelligence Technologies, Alibaba Group); Zhaoqian Shuai (Machine Intelligence Technologies, Alibaba Group); Cheng Yang (Machine Intelligence Technologies, Alibaba Group) Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including
Shumpei Okura (Yahoo! JAPAN);Yukihiro Tagami (Yahoo Japan Corporation);Shingo Ono (Yahoo Japan Corporation);Akira Tajima (Yahoo! Japan) Abstract For effective news recommendation, it is necessary to understand content of articles and preferences of users. While ID-based methods such as collaborative filtering and low rank factorization are well-known approaches for recommendation, such methods are
Denis Baylor (Google Inc.);Eric Breck (Google Inc.);Heng-Tze Cheng (Google Inc.);Noah Fiedel (Google Inc.);Chuan Yu Foo (Google Inc.);Zakaria Haque (Google Inc.);Salem Haykal (Google Inc.);Mustafa Ispir (Google Inc.);Vihan Jain (Google Inc.);Levent Koc (Google Inc.);Chiu Yuen Koo (Google Inc.);Lukasz Lew (Google Inc.);Clemens Mewald (Google Inc.);Akshay Modi (Google Inc.);Neoklis Polyzotis (Google
Daniel Golovin (Google, Inc.);Benjamin Solnik (Google, Inc.);Subhodeep Moitra (Google, Inc.);Greg Kochanski (Google, Inc.);John Karro (Google, Inc.);D. Sculley (Google, Inc.) Abstract Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand. Hence, black-box optimization has become increasingly important as systems have become more complex. I
AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called AnnexML. At training step, AnnexML constructs k-nearest neighbor graph of the label vectors and atte
Home / Topics Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank Corey Lynch, ; Kamelia Aryafar*, Etsy Inc.; Josh Attenberg, Etsy Abstract Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize ove
Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks Precise item categorization is a key issue in e-commerce domains. However, it still remains a challenging problem due to data size, category skewness, and noisy metadata. Here, we demonstrate a successful report on a deep learning-based item categorization method, i.e., deep categorization network (DeepCN), in a
PROGRAM Keynotes Plenary Panel Applied Data Science Invited Talks Applied Data Science Invited Panels Hands-On Tutorials Accepted Papers Tutorials Workshops VC Office Hours Sesssion Feedback Tag sessions and papers you want to see and get updated recommendations on the fly. Use your likes, recommendations, and other filtering options to discover your personal schedule. Log in to Confer for details
Monday, August 10th Registration desk: 7:00am - 6:00pm (Level 3 - Registration Desk) KDD15 Exhibitor Set-Up: 12:00pm - 5:00pm (Level 3 - Exhibition Area)
Twelve tutorials taught by data science experts and thought leaders. Visit the program page for their times and locations. Deep learning has rapidly moved from a marginal approach in the machine learning community less than ten years ago to one that has strong industrial impact, in particular for high-dimensional perceptual data such as speech and images, but also natural language. The demand for
Monday 8/25 Slides Tuesday 8/26 Slides Wednesday 8/27 Slides Quick Overview KDD Madness is a plenary-track fast-paced overview of all accepted papers to be presented in each day. 30-second presentation for each paper One single-page PDF slide submitted through FTP ftp://aminer.org/ with credentials: kdd14/tmp123 August 4: deadline for submitting the Madness presentation slides The Madness Session:
KDD 2014 features 5 keynotes, 151 Research Track papers, 45 Industry & Government Track papers and 8 invited talks, 12 tutorials, 25 workshops including the KDD Cup challenge, and more! KDD kicks off with a wide array of exciting events, including the KDD at Bloomberg day to Unleash Data: Accelerate Impact, the KDD Cup workshop, BPDM, full-day and half-day workshops, tutorials, the opening ceremon
SOLD OUT! KDD is SOLD OUT with record 2200 attendees! Registration is closed. Hotel rooms at the Sheraton for the night of Sat, Aug 23 were sold out. More rooms available at the Park Central NY Hotel. Special car service offer to/from airport now available from Blacklane. KDD 2014, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining,
PRESENTERS PLEASE NOTE: All research talks & best papers are no more than 20 minutes. This includes time for questions. Monday 10:30-12pm (90 min session) Research Session 1: Document and topic models • 980 One Theme in All Views: Modeling Consensus Topics in Multiple Contexts Authors: Jian Tang, Peking University; Ming Zhang, ; Qiaozhu Mei, University of Michigan • 198 Representing Documents Thro
KDD Cup 2012: User Modeling based on Microblog Data and Search Click Data This year's KDD Cup is sponsored by Tencent Inc., which is China's largest Internet company in terms of active users (over 700 Million users as of Jan. 2012). Tencent Inc. owns a full portfolio of popular products including instance messaging, email, and news portal, search engine, online games, blogging and micro-blogging
Welcome to KDD-2012: August 12-16, 2012 Beijing, China The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2012 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demons
Sunday Plenary Opening Sessions (Awards Presentation and Innovation Talk)
KDD-Cup 2011: Recommending Music Items based on the Yahoo! Music Dataset People have been fascinated by music since the dawn of humanity. A wide variety of music genres and styles has evolved, reflecting diversity in personalities, cultures and age groups. It comes as no surprise that human tastes in music are remarkably diverse, as nicely exhibited by the famous quotation: "We don't like their so
KDD2010 features high quality peer-reviewed papers on all aspects of data mining. The accepted papers are below. Research Full Presentations Research Short Presentations Industrial Full Presentations Industrial Short Presentations A Hierarchical Information Theoretic Technique for the Discovery of Non Linear Alternative Clusterings Xuan Hong Dang*, The University of Melbourne; James Bailey, The Un
Welcome to KDD-2010!The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2010 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cu
SIGKDD Sig·K·D·D \ˈsig-kā-dē-dē\ Noun (20 c) 1: The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining. 2: The community for data mining, data science and analytics Our Mission SIGKDD's mission is to provide the premier forum for advancement, education, and adoption of the "science" of knowledge discovery and data mining from all types of data store
Announcements Registration is OPEN! Click here to get started. On Monday June 29, we will hold the poster cocktail at the Hotel de Ville of Paris, in the main reception room, the Salle des Fetes, where Paris usually welcomes Heads of States and VIP The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and governme
The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-08 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition. News 26 September 2
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