Yahoo Real Time Search SMX March 2010

Yahoo! Search Real Time Search Exploring the frontiers in modern information retrieval
March 2010
Ivan Davtchev @ivan_d

What is real time search?
Showing the most relevant up-to-date content for a topic of recently increased interest.
Freshest content is great, but not always best.

Recent Y! real time search launches Tweets on SRP Improved Yahoo! News + Twitter

Challenges of real time search
Real-time indexing : get new content as it is published
Crawl really, really fast
Index news feeds, RSS, Twitter
Query analysis : discover queries to handle differently
For most queries, promoting recent content degrades relevance
Ranking for fresh content : adjust ranking algorithms
For most newly-discovered content, many traditional ranking signals do not exist or are weak (e.g. anchor text)

How do you know a query is really hot?
Find, in real-time, queries about emerging events and news stories
E.g. natural disasters; sports updates; political breaking stories; etc.
Standard approach not ideal:
Maintain temporal model for each query
Full time series or just statistics
Identify irregularities in model
Change in moving average of more than n σ’s
Works well for head queries, not so for torso/tail
Screenshots of Google Trends (©2008 Google) taken at 22:23 PST on 3/1/2010 to illustrate temporal model and tail queries.

Our Answer: Yahoo! TimeSense

How is this different and better?
It uses language modeling
Language = “collection of words”
All words in Webster’s Dictionary = English
All words in Yahoo! query logs, including misspellings = Query Log Language
Language model = a way to explain a “vocabulary” to a computer
Sentence Times seen Fraction of all sentences ebay 90000 1/1000 apple 80000 1/2000 britney spears 40000 1/5000 ebay apple britney spears 0 0

Why Language Modeling?
Language model allows us to ask questions like
“Is this word part of the language”?
Answer: if in table, yes
“Which word is more likely to appear in the language, A or B?”
Answer: whatever is higher up in the table
Is a sentence more likely to be found in text from language A or language B?
Answer: look at the table where the sentence is higher
We convert the task of classifying buzzing queries to a series of “language model questions”
And build models to answer these from query logs

Q: Is this query much more prominent right now?
To answer, we build many small language models:
One for each X minutes of query logs in the past month
Buzzing / Spiking Queries Source: Y! paper “Towards Recency Ranking in Web Search”, WSDM 2010 Current Model for last X minutes Feb. 6 Feb. 7 Feb. 8 Model for 02/07/2010 1:0Xpm

Buzzing / Spiking Queries
Q: Is this query much more prominent right now?
Language Model: Is this more likely to belong to the last X minutes than to
The previous X minutes
The same X minutes in the previous day
The same X minutes in the previous week
Etc.
Source: Y! paper “Towards Recency Ranking in Web Search”, WSDM 2010 Feb. 6 Feb. 7 Feb. 8 Current Model for last X minutes Same X minutes, previous day

There is more secret sauce of course…
Perhaps building language models for fresh content like Yahoo! News and Twitter…
And doing this very fast in production…
And then ranking content in real time – we have an interesting paper coming out soon: “Improving Recency Ranking Using Twitter Data”

Yahoo! Real Time features to look forward to
Ranking + indexing even closer to true real time
Real time results in search verticals beyond news
Real time relevance algorithms powering experiences in Yahoo! properties beyond Search

Real Time practices to avoid
Do not create content with unrelated buzz terms
Do not abuse shortening services for spam links
Do not go overboard with Twitter #hashtags
We aim to completely remove real time spam!
Screenshots taken from Twitter.com on 3/1/2010 © 2010 Twitter

Learn more
Yahoo! Search Blog:
http://www.ysearchblog.com/
@YahooSearch
Yahoo! Search Sciences:
http://labs.yahoo.com/Search_Sciences
@YahooLabs

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Yahoo Real Time Search SMX March 2010

by davtchev on Mar 02, 2010

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Yahoo Real Time Search SMX March 2010 — Presentation Transcript