PPT-Personalized Ranking Model Adaptation for Web Search

Hongning Wang 1 Xiaodong He 2 MingWei Chang 2 Yang Song 2 Ryen W White 2 and Wei Chu 3 1 Department of Computer Science University of Illinois at UrbanaChampaign. . Ipeirotis. (with . Anindya. . Ghose. and . Beibei. Li). . Leonard N. Stern School of Business. New York University . Towards a Theory Model for Product Search. Jinyun Yan. Wei Chu. Ryen White. Rutgers University. Microsoft Bing. Microsoft Research. Personalized Search. Many queries have multiple intents. e.g., [H2O] can . be a beauty . product, wireless, water, movie, band, etc.. . Schütze. and Christina . Lioma. Lecture 7: Scores in a Complete Search System. 1. Overview. Recap . . Why rank? . More on cosine. Implementation of ranking . The complete search system. 2. Steve Wiideman, Search Strategist. www.seosteve.com. . SEO 101. Relevancy. . Keyword-Themed Content. Usefulness of Content. Popularity. Mentions, Links, & Social Signals. User Behavior. Similar to Real User Monitoring (RUM). COLOR TEST. COLOR TEST. COLOR TEST. Dueling Algorithms. Nicole . Immorlica. , Northwestern University. with A. . Tauman. . Kalai. , B. . Lucier. , A. . Moitra. , A. . Postlewaite. , and M. . 1. Yang Song, . 2. Hongning Wang, . 1. Xiaodong He. 1. Microsoft Research, Redmond. . 2. University of Illinois at Urbana-Champaign. Personalized Search. Tailor search engines for each individual searcher. Jinyun Yan. Wei Chu. Ryen White. Rutgers University. Microsoft Bing. Microsoft Research. Personalized Search. Many queries have multiple intents. e.g., [H2O] can . be a beauty . product, wireless, water, movie, band, etc.. Identifying Relevant Websites. from User Activity Data. Misha Bilenko and Ryen White. presented by Matt Richardson. Microsoft Research. Search = Modeling User Behavior. Retrieval functions estimate relevance from behavior of several user groups:. 1. Yang Song, . 2. Hongning Wang, . 1. Xiaodong He. 1. Microsoft Research, Redmond. . 2. University of Illinois at Urbana-Champaign. Personalized Search. Tailor search engines for each individual searcher. Ashish Goel. Joint work with Peter Lofgren; Sid Banerjee; C . Seshadhri. 1. Personalized PageRank. 2. Assume a directed graph with . n. nodes and . m. edges. Motivation: Personalized Search. . 3. Motivation: Personalized Search. Christopher . Manning and . Pandu . Nayak. Lecture 14: Learning to Rank. Machine learning for IR ranking?. We’ve looked at methods for ranking documents in IR. Cosine similarity, inverse document frequency, . Amy Lampen. Laura Braun. Ashley Borowiak. Roy's Adaptation Model focuses on a person's . coping (adaptive) abilities. in response to a constantly changing . environment. (Lopes, Pagliuca, & Araujo, 2006).. All slides ©Addison Wesley, 2008. Retrieval Models. Provide a mathematical framework for defining the search process. includes explanation of assumptions. basis of many ranking algorithms. can be implicit. Extracting Search-Focused Key N-Grams for Relevance Ranking in Web Search Date: 2012/11/29 Author: Chen Wang, Keping Bi, Yunhua Hu, Hang Li, Guihong Cao Source: WSDM’12 Advisor: Jia -ling,