1. User Characterization in Search Personalization
RDA Data Discovery IG
SJS Khalsa
User Characterization in Search Personalization

2. Search Personalization
Keyword-based search is imperfect
Ambiguity in meaning
Varies with context
What meaning does the user assign to the term?
Personalization in information retrieval
Long history (“ask a Librarian”)
Area of intense research since advent of search engines
Commercial interest in tailoring information presented to individuals

3. User Characterization
Also called User Profiling
Through actions and inputs during search session
Search terms
Click throughs, dwell time
Response to survey questions or registration forms
Through information gleaned from other sources
Browsing history
Actions in previous sessions
Activities or profiles on social media
Affiliations (institutional, personal, foaf)
Publications, presentations

4. Creating User Profiles
Explicit (direct user intervention) information
Implicit information (monitoring user activity, inferring interests)
Static vs. dynamic
Short term vs. long term

5. User Profiles from Explicit Information
Explicit (direct user intervention) information
Information gathered at time of user registration
Response to questions/selections presented during session
Tagging, annotations, feedback forms
OrchID, ResearcherID, etc.

6. User Profiles from Implicit Information
Client-side
Browser history, bookmarks, other activity logs
Local software agents to track activity
Mining calendar entries, s, local documents (<= Privacy Issues)
General personalized environment
Server-side
Search history
Cookies (persistent)
Session IDs (ephemeral)
User behavior while interacting with site
Doesn’t capture negative feedback

7. User Profiles in Discovery
Discovery services involves both providing response to user queries as well as supporting browse (“you might also be interested in…”)
Algorithms for combining preferences from profiles with query parameters (keywords, facets)
Re-ranking
What a user identifies as relevant in the initial query result set provides feedback that an algorithm can use to re-rank the result set and possibly show new results
Relevance determined e.g. by seeing what the user selects for further inspection or by having the user tag results they feel are relevant

8. Breakouts
What should be the nature of the Data Discovery Paradigms Interest Group (DDPIG)?
Discussion group, sharing challenges, solutions and ideas
Focus on identifying bite-sized problems and spinning off WGs to find solutions (18 month timeframe)
Fostering coherence and coordination of data discovery issues in other interest and working groups, advising, contributing to use cases, etc.
Breadth of interests among membership
Strategies for entraining SMEs for focus areas

9. Possible Breakout Topics
Common API’s for data discovery: inventory and comparison
Benchmarking search results for data discovery
Overlap and interactions with other IG/WGs
Methods for content enrichment and faceted search
Algorithms for relevance ranking
Issues and guidelines for user profiling in search personalization

10. Possible focus topics Deduplication and cross-repository issues
Collections and granules: build tool that enables guidance for data submitters on how data is organized
Identifiers and how they help in search
Data citation: how do we access/use?
Guidelines for making your data findable! Best practices based on experiences.
Relevancy ranking for structured data?
Enrichment tools for faceting and ranking
Identify collections of use cases for users: e.g. browsing vs search
Domain-specific vs. generic issues: interfaces and enrichment
Measures of data quality: and impact of findability
Different discovery platforms for Open Search, science-focused OS profile?
Define series of reference datasets – can be used to do these metrics
Metadata standards to enhance data discovery, e.g. schema.org and such
Identify list of prototyping tools, use by WG!
Models and methods of personalisatoin
Cross over between domains: how to enable cross-walk between domains
Identify core elements of Findability
“Return to the semantic”: schema has been populsated by crowdsourcing rather than 1 researcher.
Automated integration of records; granularity and findability
Common APIs (e.g. OpenSearch)
Implementing schema.oirg as it exists! How does it apply to science?
Upper-level ontologies for search
Creating test collections for search evaluation and methods of evaluation