Learning to Rank for Spatiotemporal Search
Learning to Rank for Spatiotemporal Search Blake Shaw, Jon Shea, Siddhartha Sinha, Andrew Hogue Foursquare 568 Broadway New York, NY {blake, jonshea, ssinha, ahogue}@foursquare.com ABSTRACT In this article we consider the problem of mapping a noisy estimate of a user’s current location to a semantically mean- ingful point of interest, such as a home, restaurant, or store. Despite the poor accurac
Wikipedia Event Reporter
Extracting Event-Related Information From the Wikipedia Edit History Wikipedia is a multilingual, web-based, free-content encyclopedia project based on an openly editable model. It is the most up-to-date encyclopedia available. Consequently, as new events take place all over the world, Wikipedia users will update the articles corresponding to the entities involved in these events, or influenced by
Puzzling outcomes in A/B testing
A fun upcoming KDD 2012 paper out of Microsoft, "Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained" (PDF), has a lot of great insights into A/B testing and real issues you hit with A/B testing. It's a light and easy read, definitely worthwhile. Selected excerpts:We present ... puzzling outcomes of controlled experiments that we analyzed deeply to understand and explain ..
Web Search and Data Mining (WSDM) 2012
WSDM 2012 WSDM (pronounced "wisdom") is the premier international ACM conference covering research in the areas of search and data mining on the Web. The Fifth ACM WSDM Conference will take place in Seattle, Washington, USA during February 8-12, 2012. WSDM publishes original, high quality papers and presentations related to search and data mining on the Web and the Social Web, with an emphasis on
NIPS 2011 Online Papers
Advances in Neural Information Processing Systems 24 (NIPS 2011) The papers below appear in Advances in Neural Information Processing Systems 24 edited by J. Shawe-Taylor and R.S. Zemel and P.L. Bartlett and F. Pereira and K.Q. Weinberger. They are proceedings from the conference, "Neural Information Processing Systems 2011." Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kama
Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting
Amr Ahmed is a Senior Staff Research Scientist at Google. He received his M.Sc and PhD degrees from the School of Computer Science, Carnegie Mellon University in 2009 and 2011, respectively. He received the best paper award at KDD 2014 , the best Paper Award at WSDM 2014, the 2012 ACM SIGKDD Doctoral Dissertation Award, and a best paper award (runner-up) at WSDM 2012. He co-chaired the WWW'18 trac
Flavor network and the principles of food pairing - Scientific Reports
As omnivores, humans have historically faced the difficult task of identifying and gathering food that satisfies nutritional needs while avoiding foodborne illnesses1. This process has contributed to the current diet of humans, which is influenced by factors ranging from an evolved preference for sugar and fat to palatability, nutritional value, culture, ease of production and climate1,2,3,4,5,6,7
www上ã§é¢¨èª¬ã®æµå¸ƒã€èª¤è§£å½¢æˆã®ãƒ¡ã‚«ãƒ‹ã‚ºãƒ : ç ”ç©¶é–‹ç™º
ç·åˆç ”究大å¦é™¢å¤§å¦ã€€è¤‡åˆç§‘å¦ç ”究科　 æƒ…å ±å¦å°‚攻　å’　åšå£«ï¼ˆæƒ…å ±å¦ï¼‰ 自然言語処ç†ã‚„機械å¦ç¿’ã€ãƒ‡ãƒ¼ã‚¿åˆ†æžã«é–¢ã™ã‚‹ç ”究内容ã¨webシステムã®é–‹ç™ºã¨é‹ç”¨ã«ã¤ã„ã¦æ›¸ã„ã¦ã„ã¾ã™ã€‚ シリコンãƒãƒ¬ãƒ¼ãƒ™ãƒ³ãƒãƒ£ãƒ¼ã¿ãŸã„ã«æ·±ã„技術ã®äº‹æ¥åŒ–ã‚’ã—ãŸã„ã¨æ€ã£ã¦ã„ã¾ã™ã€‚ ã”興味ã‚ã‚‹æ–¹ã¯ã”連絡ãã ã•ã„。 匿åコミュニティã«ãŠã‘る対人èªçŸ¥ 畦地真太郎 北海é“æ±æµ·å¤§å¦ æ¾æ‘憲一 独立行政法人通信ç·åˆç ”究所 MYCOM2002 匿åコミュニティã«ãŠã‘る，もã†ä¸€ã¤ã®å•é¡Œã¯ï¼Œã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£ã®æˆå“¡ãŒæ„見分布をæ£ã—ãèªçŸ¥ã§ããªã„ã¨ã„ã†ã“ã¨ã§ã‚る．例ãˆã°ï¼Œäººã¯è‡ªåˆ†ã¨åŒã˜æ„見ã®æŒã¡ä¸»ãŒç¤¾ä¼šå…¨ä½“ã«å®Ÿéš›ã‚ˆã‚Šã‚‚多ãå˜åœ¨ã™ã‚‹ã¨èªçŸ¥ã™ã‚‹å‚¾å‘ãŒã‚ã‚‹ã¨ã„ã†ã“ã¨ãŒçŸ¥ã‚‰ã‚Œã¦ã„る． ã“ã®â€œèª¤ã£ãŸåˆæ„性èªçŸ¥ï¼ˆFaulse Consensus Effect）†[The ‘false consensus effect’: An egocentric bias
PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline Processing ZHIYUAN LIU, YUZHOU ZHANG, and EDWARD Y. CHANG, Google Inc. MAOSONG SUN, Tsinghua University Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies: data placement, pipeline processing, word bundling, and p
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
A Uniï¬ed Search Federation System Based on Online User Feedback
Absence Time and User Engagement: Evaluating Ranking Functions
To Crop, Or Not to Crop: Compiling Online Media Galleries
Discovering Value from Community Activity on Focused Question Answering Sites: A Case Study of Stack Overflow
User Demographics and Language in an Implicit Social Network
CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering
Proceedings of the SIGIR 2012 Workshop on Open Source Information Retrieval
Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained
Learning to Rank with Multi-Aspect Relevance for Vertical Search
Correlating Financial Time Series with Micro-Blogging Activity
http://ai.stanford.edu/~rajatr/papers/sigir11wsim.pdf
{{#tags}}- {{label}}
{{/tags}}