Better Search UX
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The document outlines the redesign of the search function for Baker Library, including the integration of search results from Baker and HBS to enhance user experience. It details the technical implementation using Ultraseek, customization of the search results display, and the analytics tracked for search usage. The iterative approach allowed for fine-grained control and met the business requirements without needing additional software.
![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’,...]](https://image.slidesharecdn.com/bettersearchux-110421114412-phpapp02/85/Better-Search-UX-14-320.jpg)
![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']](https://image.slidesharecdn.com/bettersearchux-110421114412-phpapp02/85/Better-Search-UX-20-320.jpg)
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
- 5. Old SERP
- 6. New SERP
- 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
- 17. Old SERP
- 18. New SERP
- 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