slide1 l.
Download
Skip this Video
Download Presentation
Mobile Search and Advertisement Cache Architecture

Loading in 2 Seconds...

play fullscreen
1 / 1

Mobile Search and Advertisement Cache Architecture - PowerPoint PPT Presentation


  • 118 Views
  • Uploaded on

Mobile Search and Advertisement Cache Architecture. Dimitrios Lymberopoulos, Emmanouil Koukoumidis, Jie Liu, Doug Burger (MSR Redmond) Varun Kansal, Chen Xia, Kenny Chien, Bin Wu, Fang Wang, Melissa Dunn (MAXPLAT). Mobile Search Experience. Push Search/Ads to the Phone. Performance Bottleneck.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Mobile Search and Advertisement Cache Architecture' - mayes


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Mobile Search and Advertisement Cache Architecture

Dimitrios Lymberopoulos, Emmanouil Koukoumidis, Jie Liu, Doug Burger (MSR Redmond)

Varun Kansal, Chen Xia, Kenny Chien, Bin Wu, Fang Wang, Melissa Dunn (MAXPLAT)

Mobile Search Experience

Push Search/Ads to the Phone

Performance Bottleneck

Data

Sensing

Web

m.bing.com

Data

Search/Ad Request

Community+

Personal

Data

Ads

Data

3G

indexes

Subjective Cache

Context

+

Personalization

results

Phone

Cloud

  • 3G link is slow
  • Average end-to-end delay: 4-8 seconds
  • 3G connection time: 10 - 30 seconds
  • Challenges
  • What information do we cache?
  • How do we cache it?
  • How do we manage it over time?
  • Leverage mobile trends
  • Increasing flash density at lower cost
  • Increasing processing power

Goal: Faster Mobile Search Experience

SONGO (Search ON the GO): A Data-driven Architecture

Mobile Search Log Analysis - 100M Queries

Fetch links

Results Page Construction

results

Ranking

Rank links based on their quality

user clicks

Link Storage

(Flash ~ 1-2MB)

cache hit

results

cache hit

Personalization

query

Community

Aggregate Volume (%)

SONGO cache

query

Percentage of Unique Users

Phone

periodic updates

cache miss

m.bing.com

Hash Table

(RAM ~ 200KB)

Adjust Ranking Scores

SONGO cache

Up-to-date Community Cache

60% of mobile query volume hits 6K queries and 4K links!

50% of the users repeat a query at least 70% of the time!

user clicks

Number of query-link pairs

Probability of a new query-link pair

0.links

15.links

31.links

Experimental Prototypes

Beyond Web Search

On average 66% of the queries a user submits hit the cache

  • Real-time:
  • search results
  • business lookups
  • ad delivery
  • Opportunities:
  • Faster user experience
  • Monetization of autosuggest
  • Fastest mobile ad delivery engine
  • Personalized ranking for search/ads
  • Privacy: profile on the phone

23x more energy efficient!

16x faster!