1. Cloud Platform Group (CPG)
Presentation at IIT Chennai
March 29, 2012

2. Agenda
 CPG Mission and Value Proposition
 Fit within the Yahoo Stack
 Drill-down: User Generated Content (UGC)
 Drill-down: User Location
 Drill-down: Web Extractions
 Drill-down: Trending
 Q&A
Yahoo! Presentation, Confidential 2

3. Cloud Platform Group Mission
Create a global, scalable platform built on
science that enables rapid innovation and
delivery of personalized, monetizable
experiences across devices.
Yahoo! Presentation, Confidential 3 3/29/2012

4. CPG Value Proposition
1 Agility with Stability
LEGO powered by Content Agility
Yahoo! Presentation, Confidential 4

5. CPG Value Proposition
2 Science at Scale
Yahoo! Presentation, Confidential 5

6. ILLUSTRATIVE SAMPLE
CPG powers all of Yahoo! today
MAIL
DISPLAY ADS FRONT PAGE
powered by Edge,
powered by Hadoop powered by CORE
Storage, Ranking, & Hadoop
3x improvement in accuracy of ad 40% faster download time, 300K+ spam Increased CTR by +263% for Today
placements and our ability to forecast mails blocked/ sec Module by serving right content to the
supply over legacy systems right user (over pre-CORE)
LIVESTAND
LEGO (YPP) SOCIAL CHROME
powered by Mobile &
powered by Content Agility powered by Social Platform
Cocktails Presentation Services
Reduce time to launch new sites from Over 22M net cumulative installs Seamlessly distribute content across
quarters to weeks since launch, Integrated into devices in an experience that is
News, Games, Movies, OMG, TV elegant and personalized
Yahoo! Presentation, Confidential 6

7. User Generated Content
Unified, scalable platform that enables self expression and gets users to
connect over content
USE CASE RESULTS
Increase content stickiness UGC platforms are used by over 200 Yahoo!
and user retention; drive properties with over 650M UGC actions per year
repeat usage across the Comments Message Boards
Yahoo! network
1/3 of US
6M
Finance
comments
traffic
per month
from MB
Ratings & Reviews
Polls
SOLUTION
40M user
UGC Cloud is a ratings
scalable, real-time platform 1.2M poll per
votes per month
that lets users to express month
themselves, resulting in
increased user
engagement and a vibrant
Yahoo! community

8. User Generated Content – Applications
Improving Comment Quality
3 pronged approach – Machine; Human and Community Moderation
300M analyzed, 70 M blocked with machine moderation
Reactive Volume (cost of reacting to abuse) avoided
Sentiment Slider
http://news.yahoo.com/open-business-free-agency-set-begin-211828913--spt.html

9. User Generated Content – Social Poll

10. User Generated Content – In the Works
Topical Organization of Comments Social Conversations

11. User Location
Store, manage & share user locations and locations of interest to create
deeply personal digital experiences
USE CASE RESULTS
User location information was Properties can launch location aware services
siloed, inconsistent, and with faster time to market on a single platform
not shareable across
properties and users 237M users with 550M locations
Management, Authorization, and Control
LOCDROP
Normalized, Geo-Aware User Locations
SOLUTION Centralized, Consistent, and Contextual
Accurate, Relevant, Valuable Experiences
Create a single data store Increase Content, Targeting and Revenues
of user locations, shareable
across Yahoo! properties and
advertising systems

12. Read locations to drive local news, events and deals

13. Contextual Locations for Yahoo News
YAHOO! CONFIDENTIAL

14. User Generated Places: Enable users to submit (and curate) a
location if one does not exist
Android Messenger Use Case
User cannot find a place and decides to create
a new location to check-in
User is asked for permission to detect current
location from device
Users location is pointed on a map. This will be
used to get the lat/long of the created place
User enters a location “Russian Tea Room”
A new location is stored in UGP platform and
the user is checked-in to this location
User has an option to curate the locations
created by other users
UGP platform enables algorithmic curation

15. KAFE: Technologies*
Web Content
Manual SDE Rules
Bing WCC YST HVC Live Pages
 Large Aggregator Websites (LLFS)
(e.g. amazon)
Editorial
Effort
Dapper KAFE
 Small Websites
(e.g. community sites) S.D.E Dapper PSOX
 Behind the Form sites
(Deep Web)
PSOX (Y! Labs)
 Unsupervised extractions
from large number of
websites
W.O.O Properties
 Goldrush, Dish-a- Legacy
wish, Restaurant Photos Backend
Precision
* Supports Multiple Sources of Data and Multiple Technologies
Yahoo! Presentation, Confidential 15

16. Answers Not Links
Dappfactory
Dappfactory used by DD Builder to create over 3000+ DD experiences !
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17. Answers Not Links
Dappfactory
Dappfactory used by DD Builder to create over 3000+ DD experiences !
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18. Answers Not Links
S-DEKAFE XSL Rules
Creating Vertical Search Experiences for
Recipes
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19. Answers Not Links
PSOX-Unsupervised Extractions
Looking for where to buy Amana dishwashers ? Y! Goldrush
Craving for Hummus in Sunnyvale ? Y! Dish-a-Wish
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20. Enhanced Listings
Dappfactory
Before:
After:
• Taken from Roadmap deck for Y! Local by Erin Johns
• Data being provided to Y! Local, Front End revamp on Local Roadmap
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21. Local Events for N.I.L.E Dappfactory
Extracted using
Dappfactory
As of Feb ‘12, over 22,000 events for 250 US cities have been extracted using Dappfactory
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22. Data Extraction – Challenges
 Technology whitespace
 Head – Fully manual scales fine. Gives high precision.
 Torso – Mostly use human assisted learning. Drop in recall and
precision, but acceptable for production use.
 Tail content – Only option is ML/no-human-in-loop models.
Recall and Precision need lot of improvement.
 Semantic Web initiatives – Web of Objects
 Linked Open Data Format (RDF-a, OWL, Sparql)
 Lod Cloud – Few Thousand data sets, 10s of billions of
interlinked facts.
 Confhopper – Sample/Demo application
 Unstructured Corpus – NLP Extraction
 Systems /Engineering Challenges – Low Latency
processing, tokenization/parsing – Intl support
 Sciences Challenges –
polysemy, synonymy, aboutness/concepts, sentiment analysis.
 CAP – Contextual analysis platform
Yahoo! Presentation, Confidential 22

23. TimeSense – usecases/business value proposition
Search Suggestions in SD box – Timesense powered
US FP Trending Now local pool for a given DMA suggestions triggered for 6% of all gossip requests
powered by TS –6% CTR lift attributed to local terms
Trending searches in Left Rail on Yahoo US SRP – triggered
for ~6% of all user queries
TW FP Trending Now automated by
Timesense API
Plumbing, Monetization, & Games
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24. TimeSense
In Bucket
AUTOMATED trending module on shopping.yahoo.com : First module with no editorial intervention, vertically categorized
trends, fast refresh and rotating terms
Soon to Launch
HK , TW and KR Automated trends modules on FP, Mail, OMG, news etc
Editorial Power users of Timesense
• Search Forecasting Editorial Team – updates sent twice a day to 500+ subscribers
• FP Trending Now team
Plumbing, Monetization, & Games
•Regional Content programming , search editorial and SEO teams : US ,UK, HK, TW, IN [Q1 launch – all INTLs]
Upcoming
• Trending Now Syndication for Yahoo Hosted Search partners – via BOSS
• Trending Image experience
• Trending Now 2.0 automation expansion 24

25. Trending topic detection – Challenges
 Systems Challenges
• Low latency requirement
• GBs of data analyzed from multiple data sources every 5
minutes
• Scalability – different verticals, segmented models.
• High Availability requirement
 Sciences Challenges
 Algorithmic improvements for near real time detection without
precision loss
 Short Phrase Categorization
 Deduping/Clustering – intent detection
 Segmentation/Smoothing – Age/gender/Behavioral Tracking
Categories/Geography – signal sparsity with fine grained
segmentation.
Yahoo! Presentation, Confidential 25