Yahoo! ResearchBradley Horowitz

VP Product StrategyYahoo, Inc.August 2006

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Areas of Research

• Community: – How do you know what to believe on the Internet?– Trust models on-line and trust propagation– What makes communities thrive? Whither?– Social media, tagging, image and video sharing

• Microeconomics: a new generation of economics driven by massive interactions– Auction marketplaces– The web as a new hub of activities and economies

• Information Navigation and Search– We are in the early days of search and retrieval

• User Experience

“Better Search Through People”

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Great content?Great content

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People + Algorithms > Algorithms

• Phase I – human editorial• Phase II – mass automation• Phase III – topological analysis

• Phase IV – Social Searcheditorial + automation + topology

Social Search @ Yahoo: My Web, Yahoo! Answers, del.icio.us, flickr, upcoming.org, etc.

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1995: The Yahoo! Directory

• Apply human expertise and editorial to organize web sites

• What worked– Practical, Navigable– Trustworthy, Authoritative

• What didn’t– Scalability– Granularity– Etc.

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1995 : Altavista (Inktomi, Lycos, etc.)

• Automate the process of acquiring pages; use “information retrieval” techniques to return pages that contain a particular term

• What worked– Scalable (query for “IBM” returns 40M pages)– Simple– Granular

• What didn’t– Scalability a double-edged sword– Ranking and relevance poor– Not authoritative (spam, irrelevance, etc.)

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c. 1999-2006: PageRank (Google, Yahoo)

• Use topology (link structure) of the web to confer authority

• What works– Relevance is greatly improved

– Navigational query is born (query for “IBM” gets me to ibm.com)

• What doesn’t– Homogeneity of results (no personalization) means no “subjective”

queries – webmasters vote by proxy for everyone – and their answer is the only answer

– System easily “gamed” by spammers – leads to arms race

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Query composition

•~40% Navigational

•~35% Transactional

~25% Informational

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Subjective Queries

The kinds of queries that rely on domain expertise…

• “Do you know a reputable plumber in London?”• “Where is the cool nightlife in Mayfair?”• “What political blogs do you think I’d enjoy reading?”• “Where can I buy a cool pair of boots?”

These kinds of queries are ill-served by today’s search engines, but are ironically the most valuable (i.e. transactional queries.)

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Yahoo!’s Focus: Social Search

• Democratize process of “voting” (whether explicit or implicit)

• Move out of the purview of webmasters and hand control back to users

• Allow dynamic assignment to various authorities of trust, new degree of freedom

“Better Search Through People”

Yahoo! Research Prototypes

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ZoneTag

• Key Points:– Use context available from mobile devices to help create, find,

discover, share media– Provide least effort, most benefits

• Explore the future of media• Yahoo! Research Berkeley

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Media Remixer SF International Film Festival

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Filmclips

How it works (High level design)

Web server

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Remixer Flash media server

1. User goes to SFIFF website and starts the web-based Flash remix player. User selects “penguin” clip and chooses time segment 00:01:10 to 00:01:20.

2. Player sends the user request to the server. Server finds the clip and the requested segment within the clip.

3. Server streams the clip to the player and to the user.

4. Process continues as user compiles a playlist of clips and segments

Segmenter

SFIFF web page

1Web-basedFlash remix

player3

need screenshot or mock-up

YRB prototype services

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Screenshot

“Better Search Through People”Case Study: Yahoo! Answers

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Making Connections

• Today’s state-of-the-art web search (at best) connects a person with a query to a web page that contains those characters

• Even as we nobly try to index every page, (every book, every video, every podcast, etc.) it is worth noting that these are artifacts of human knowledge

• Yahoo! Answers addresses this problem. It connects a person with a question to a community of people best suited to answer the question.

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Thank You

bradleyh@yahoo-inc.com

“Better Search Through People”

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Community Dynamics

1 creators

10 synthesizers

100 consumers

2 creators

4 synthesizers

8 consumers

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Community Dynamics

100% creators

100% synthesizers

100% consumers

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A Digression:Computer Vision is hard

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“Better Search Through People”Case Study: del.icio.us

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“Better Search Through People”Case Study: MyWeb 2.0