云计算时代得社交网络.ppt

Shaighai Offsite Google Confidential Ed Chang 云计算时代的社交网络平台和技术 张智威 副院长, 研究院, 谷歌中国 教授, 电机工程系, 加州大学 China Opportunity China US in 2006-07 Google China Size (~700) 200 engineers 400 other employees Almost 100 interns Locations Beijing (2005) Taipei (2006) Shanghai (2007) Organizing the World’s Information, Socially 社区平台 (Social Platform) 云运算 (Cloud Computing) 结论与前瞻 (Concluding Remarks) Web 1.0 Web with People (2.0) + Social Platforms 开放社区平台 开放社区平台 开放社区平台 Social Graph What Users Want? People care about other people care about people they know connect to people they do not know Discover interesting information based on other people about who other people are about what other people are doing Information Overflow Challenge Too many people, too many choices of forums and apps “I soon need to hire a full-time to manage my online social networks” Desiring a Social Network Recommendation System Recommendation System Friend Recommendation Community/Forum Recommendation Application Suggestion Ads Matching Organizing the World’s Information, Socially 社区平台 (Social Platform) 云运算 (Cloud Computing) 结论与前瞻 (Concluding Remarks) Collaborative Filtering Collaborative Filtering (CF)[Breese, Heckerman and Kadie 1998] Memory-based Given user u, find “similar” users (k nearest neighbors) Bought similar items, saw similar movies, similar profiles, etc. Different similarity measures yield different techniques Make predictions based on the preferences of these “similar” users Model-based Build a model of relationship between subject matters Make predictions based on the constructed model Memory-Based Model [Goldbert et al. 1992; Resnik et al. 1994; Konstant et al. 1997] Pros Simplicity, avoid model-building stage Cons Memory and Time consuming, uses the entire database every time to make a prediction Cannot make prediction if the user has no items in common with other users Model-Based Model[Breese et al. 1998; Hoffman 1999; Blei et al. 2004] Pros Scalability, model is much