This document discusses Google Scholar's approach to focusing on individual authors by building author profiles to disambiguate authors, integrate search results, and provide personalized recommendations. The key aspects are author disambiguation using a statistical model, integration of author profiles into search, and personalized recommendations based on author interests and citation graphs. The approach has seen worldwide adoption and allows many useful services by taking only a few minutes of initial effort from authors.

1. Focusing on the Individual:
Find, Build, Suggest
Anurag Acharya

2. Science is anchored by scientists
 Keeping up with significant advances
– We look for recent work by key authors
 Evaluating quality of new research
– We identify & invite experts to review
 Funding impactful research
– We look for investigators with a proven record

3. Scholars on Google Scholar
 Challenges
– Identify publications by an author
– Keep them up-to-date
– Pivot from articles to authors and back
 Opportunities
– Find me what I need to read
– Identify key researchers in areas

4. Overview
 Author disambiguation
– Quickly setup author profile, auto-maintain
 Integration with Scholar search
– Work by key authors
– Find experts in an area
 Personalized recommendations
– Find me what I need to read

5. Author disambiguation approach
 Build a statistical model grouping articles
by an author with the same name
– Author lists, journals, co-authors, research
area, affiliations, text of articles
– Multi-dimensional model
– Shoot for high precision, very good recall

6. Disambiguation approach – II
 Many authors work in multiple areas
– With multiple co-authors, multiple communities
– Trying to group these results will break others
 Allow such groups to remain separate
– Make it trivially easy for authors to merge
 Final disambiguation step is human
– Key is to make the human step very simple

7. How does it work?
 Present groups of articles matching name
– Author selects groups written by her
 After setup, updates are automated
 Author can merge/add/remove
 Changes fed back into the statistical model
– Improves update precision & recall
– Automated => must be hi-precision/hi-recall

9. What do you get?
 List of all your publications
 Citation metrics – overall and per article
 Links to co-authors
 Follow all your citations
 Colleagues can follow your work
 Personalized recommendations

12. How well does it work?
 Worldwide adoption
– Widely published and cited authors
– Most authors take a few minutes
– Most authors opt for automated updates
– All countries, all areas

16. Why does it work?
 Statistical model is quite effective
– Able to achieve high precision
– Recall tradeoff is small
– Flexibility to split groups key, but not frequent
 For most authors takes 5-10 minutes
– Effort is one-time, updates are automated
 Enables many useful services – well worth
the few minutes!

17. Finding Scholars
 Queries including name
– Add matching author profiles as results
 Keyword queries
– Link author names to author profiles
 Browse researchers in an area
– Link author interests to search over profiles

21. Personalized recommendations
 Challenge: keep up with rapid growth in
articles
 Approach:
– Centered around author profiles
– Analyze research interests & evolution
– Factor in co-authors & their evolution
– Leverage citation graph
– Scan all newly seen articles

25. Finally…
 Individuals key for recovering structure
– Allow efficient, cascade-able disambiguation
– Depts, institutions, funding agencies
– Most other analyses can be layered on top
 Find what I need to read – classic hard
problem
– Much progress, much much more to do