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Better Search UX
         Using a
No-frills Search Engine




  Ravi Mynampaty
    @ravimynampaty
Approach

• Tactical

• Baker Library site being redesigned

• So, redesigned search function
     – including Search Engine Results Page
       (SERP)

• Iterative
Motivation
• Merge Baker and HBS search results
     – Allow users to easily switch context
     – Allow views by category / department

• Provide mechanisms to resubmit query to other
  search systems

• Implement common look & feel for Baker Website
  and SERP

• Increase search usage
Background

• Set up at HBS
     – Ultraseek 5
     – Load balanced environment
     – Development instance

• Ultraseek
      – Results display UI based on Python
      – Built-in HTTP server
      – Web admin interface
Old SERP
New SERP
Ultraseek SERP Customization: Some Options
•   Use Java API or RPC Interface

•   HTTP GET results as XML (saquery.xml) or plain text
    (saquery.spy)
       – parse, format & serve

•   HTTP GET results as Python data structure
    (pyquery.spy)
       – eval, format & serve

•   Modify the Ultraseek results display code (query.html)
      – use Python API calls therein
First Steps

• Backed up default display code
     – esp. query.html

• Created our version of query.html

• Started with…

<!--$self.write ('Hello World!')-->
Customized query.html


• Pulled in headers/footers from Baker Website
header =
  urllib.urlopen(header_url).read()

• Mapped collections to tabs
    Baker Library           +url:"library.hbs.edu"
    HBS WK                  +url:"hbswk.hbs.edu“
Built the SERP


• In query.html used Ultraseek Python API to
      – submit query
      – retrieve results

• Displayed results with HTML markup & CSS
Added Analytics Component

• Tracked usage of tabs, query resubmits, etc.

• Logged everything to the Ultraseek logs using
  Ultraseek’s logging mechanisms

• Tracked everything (even Search Tips page!)
13,956 Total Searches
                       (Sept – Dec)




3%   11%    10%


           3%




                                8%
Search Resubmits: Popularity



          256

          167

          151

          423
  ***
          71



“Next Page” usage: Page 1 (94%), Page 2 (4%)
Extended to Other HBS Sites


• Alumni, Exec Ed, HBS Working Knowledge
     – with different sets of tabs, resubmits

• Modifed query.html to be driven off config files
config[‘library'][‘tabs']
  = [‘Baker Website’, ‘HBSWK’, ...]

config[‘alumni'][‘tabs']
  = [‘Alumni Website’, ‘Bulletin’,...]
Better Search UX
Moved on to Main HBS Search

• Horizontal tab paradigm didn’t fit

• Used a vertical tab / right-hand nav model

• Allow result refinement…
      – to slices within HBS

• …and expansion
    – to the rest of Harvard University
Old SERP
New SERP
Other Harvard Web Refinement Option
Pulled In Other Harvard Web Content Results
• Defined the external Ultraseek URL
url =
  ‘http://...:8765/pyquery.spy?qt=term’

• Executed search and downloaded results
results = urllib.urlopen(url).read()

• Built and accessed data structure
results_hash = eval(results)
num_hits = results_hash['nhits']
Conclusion
• Tactical, iterative approach worked

• Fine-grain control over:
     – Display (tabs, hit counts)
     – Behavior (collections, default AND)
     – Clean URLs

• No additional software component

• Customization met business needs

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Better Search UX

  • 1. Better Search UX Using a No-frills Search Engine Ravi Mynampaty @ravimynampaty
  • 2. Approach • Tactical • Baker Library site being redesigned • So, redesigned search function – including Search Engine Results Page (SERP) • Iterative
  • 3. Motivation • Merge Baker and HBS search results – Allow users to easily switch context – Allow views by category / department • Provide mechanisms to resubmit query to other search systems • Implement common look & feel for Baker Website and SERP • Increase search usage
  • 4. Background • Set up at HBS – Ultraseek 5 – Load balanced environment – Development instance • Ultraseek – Results display UI based on Python – Built-in HTTP server – Web admin interface
  • 7. Ultraseek SERP Customization: Some Options • Use Java API or RPC Interface • HTTP GET results as XML (saquery.xml) or plain text (saquery.spy) – parse, format & serve • HTTP GET results as Python data structure (pyquery.spy) – eval, format & serve • Modify the Ultraseek results display code (query.html) – use Python API calls therein
  • 8. First Steps • Backed up default display code – esp. query.html • Created our version of query.html • Started with… <!--$self.write ('Hello World!')-->
  • 9. Customized query.html • Pulled in headers/footers from Baker Website header = urllib.urlopen(header_url).read() • Mapped collections to tabs Baker Library  +url:"library.hbs.edu" HBS WK  +url:"hbswk.hbs.edu“
  • 10. Built the SERP • In query.html used Ultraseek Python API to – submit query – retrieve results • Displayed results with HTML markup & CSS
  • 11. Added Analytics Component • Tracked usage of tabs, query resubmits, etc. • Logged everything to the Ultraseek logs using Ultraseek’s logging mechanisms • Tracked everything (even Search Tips page!)
  • 12. 13,956 Total Searches (Sept – Dec) 3% 11% 10% 3% 8%
  • 13. Search Resubmits: Popularity 256 167 151 423 *** 71 “Next Page” usage: Page 1 (94%), Page 2 (4%)
  • 14. Extended to Other HBS Sites • Alumni, Exec Ed, HBS Working Knowledge – with different sets of tabs, resubmits • Modifed query.html to be driven off config files config[‘library'][‘tabs'] = [‘Baker Website’, ‘HBSWK’, ...] config[‘alumni'][‘tabs'] = [‘Alumni Website’, ‘Bulletin’,...]
  • 16. Moved on to Main HBS Search • Horizontal tab paradigm didn’t fit • Used a vertical tab / right-hand nav model • Allow result refinement… – to slices within HBS • …and expansion – to the rest of Harvard University
  • 19. Other Harvard Web Refinement Option
  • 20. Pulled In Other Harvard Web Content Results • Defined the external Ultraseek URL url = ‘http://...:8765/pyquery.spy?qt=term’ • Executed search and downloaded results results = urllib.urlopen(url).read() • Built and accessed data structure results_hash = eval(results) num_hits = results_hash['nhits']
  • 21. Conclusion • Tactical, iterative approach worked • Fine-grain control over: – Display (tabs, hit counts) – Behavior (collections, default AND) – Clean URLs • No additional software component • Customization met business needs