Over the past two decades, several information exploration approaches were suggested to support a special category of search tasks known as exploratory search.These approaches creatively combined search, browsing, and information analysis steps shifting user efforts from recall (formulating a query) to recognition (i.e., selecting a link) and helping them to gradually learn more about the explored domain. More recently, a few projects demonstrated that personalizing the process of information exploration with models of user interests can add value to information exploration systems. However, the current model-based information exploration interfaces are very sophisticated and focus on highly experienced users. The project presented in this paper attempted to assess the value of open user modeling in supporting personalized information exploration by novice users.We present an information exploration system with an open and controllable user model, which supports undergraduate students in finding research advisors. A controlled study of this system with target users demonstrated its advantage over a traditional search interface and revealed interesting aspects of user behavior in a model-based interface.

1. Personalizing Information Exploration
with an Open User Model
Behnam Rahdari, Peter Brusilovsky and Dmitriy Babichenko
School of Competing and information – University of Pittsburgh
31st ACM Conference on Hypertext and Social Media (HT’20)

2. • Google alone is responsible for 5.4 billion search per day
• Complexity:
• Simple: “what day is today?” – Only one right answer
• Complex: “where is the closest four-star Italian restaurant near me?” –
Personalized
• Nature of the search:
• A novice user search for a new computer:
• Laptop? Desktop?
• Computational power? How many CPU cores?
• Memory capacity? SSD? GPU? etc.…
Search and Beyond 01

3. • “Exploratory search is a specialization of information exploration
which represents the activities carried out by searchers who
are”[1]:
1. Unfamiliar with the domain of their goal.
2. Unsure about the ways to achieve their goals.
3. Unsure about their goals in the first place.
• There are a number of exploratory search systems
• How we can make it better?
[1] - Ryen W. White and Resa A. Roth (2009). Exploratory Search: Beyond the Query-Response Paradigm, San Rafael, CA: Morgan and Claypool.
Exploratory Search 02

4. Proposed Approach
• A 15 years of old Idea + A number of critical technologies
• Make a more powerful tool
• More tailored to Novice Users need
• Use Case:
• Finding Research Advisor
• For undergraduate level
• Has all the characteristics
• A real problem to solve Concept
Extraction
Graph-
powered
Network
Visual
Exploratory
Search
Results
Explanat
ion
user
control
Interfaces
User
Profiling
03

5. • Over 1.5 years of research
• Multiple system iterations and rounds of evaluation
Background 04

6. Open User Profile
Keyword
recommendation
Final Results Recommended Advisors
Interface Design – Main Interface 05

7. Interface Design – Dialogs
Details View Wikipedia Summary
06

8. • Inter-connected graph: Google scholar entities + Wikipedia
Enrichment
Weighted
Relationship
Knowledge Graph 07

9. • Advisor recommendation
• Similar Keyword Recommendation
• Collaborative filtering: co-authorship, Wikipedia Links and Categories
Slider value
similarity
Recommendation Approach 08

10. • Multiple keywords in a single query ( Like Google Search)
• No Open User Model (Keyword accumulation and Slider)
• No Keyword recommendations
Evaluation - Baseline 09

11. • 1000 highly cited researchers (Google Scholar)
• Artificial Intelligence
• Computer Architecture
• Extracted Data:
• Name, affiliation, number of citations, h-index, i10-index, etc.
• 20 Most recent publications (for the purpose of keyword extraction)
• Top 10 co-authors (for the purpose of social connection)
• Enriched with Wikipedia API
Evaluation – Data Source 10

12. • 42 Students: Python for Data Management & Analytics (INFSCI
0019)
• Average age of 21.30 (SD: 2.00)
• Mostly senior undergraduate: N = 38
• Program of study:
• Computer science: N = 33
• Information science: N = 3
• Others: N = 6
• Participants were not compensated for participation
Evaluation – Participants 11

13. • whether the new exploratory and profile-tuning features
were embraced by the target users
• how the presence of these features changed their
exploration behavior
• whether the new design lead to better exploration
experience.
Research Questions 12

14. Results: Profile and Query Building
• Frequency of using each feature
• Directly added keywords vs exploration
• Total number of interactions with the system
13

15. Results – Behavioral Change
External Resources Search vs Review
14

16. Results – User Experience
Post-Task Survey Results (All Users)
Explorers : Profile Size: 4.38
Neutrals : Profile Size: 3.18
Searchers: Profile Size: 2.90
15

17. Summary and Future Works
• We built an Exploratory Search System that:
• Recommends research advisor to students
• Has an open user model
• Combines multiple technologies
• Outperforms the baseline
• Future Works:
• Including more entities in the graph
• User study with more diverse group of participants
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