Developing Recommendation Services for a Digital Library Recommendations in iLumina with Uncertain and Changing Data Gary Geisler (UNC), Dave McArthur and Sarah Giersch (Eduprise) Funding provided by the National Science Foundation DLI-Phase 2 NSF Award #0002935 A Digital Library of Reusable Science and Math Resources for Undergraduate Education iLumina’s Vision The situation: • e-learning will create a huge demand for high-quality digital course content • Publishers will meet some of this demand through ebooks • Instructors will still need informal digital modules to complement and tailor ebooks to specific courses The opportunity: • The Internet now provides a scalable way to implement peer- centric sharing of informal digital content • iLumina is a digital library that will realize this opportunity iLumina’s Main Themes • Featuring diverse, small-scale SMETE resources, especially ones created by instructors • Using IMS’s rich and standard metadata to describe resources • Developing services underpinned by IMS metadata (e.g., recommendations as well as search and browse) • Promoting reuse and repackaging of resources by faculty authors • Implementing a partially centralized (metadata) and partly distributed (content) architecture Architecture Overview Existing Repositories Informal Collections Individual Contributions … … Addison CSTC SECDL Other UNCW Mathwright Other iLumina Open Physlets Wesley * Cataloging Tool Mapping Tool Content Review * Cataloging Tool final Basic Search Faceted Search iLumina Browsing metadata Flexible Retrieval Search Services Construction Author Cataloging Resource Integrator Suggestion Boxes Services General forums User User ratings/review Community Formal peer review Services Recommendations Personalizations Information Sources for Recommendations Data Type Description Quality Availability Resource IMS metadata elements and High Complete Characteristics values Resource Minimal acceptability judgments Variable Partial Evaluation Informal user ratings Formal peer reviews Resource User downloads Low Partial Usage User resubmissions User Profile Demographics Variable Partial Affiliations Areas of interest Service preferences Recommendation Schemes Scheme Name Sources Rule Simple Winner Evaluation (User If a resource is highly rated, ratings or reviews) recommend it. Profile Match Characteristics & If a resource matches areas of Profile interests, recommend it Basic Content- Self ratings & If a resource is structurally similar based Characteristics to ones previously liked, recommend it. Basic Collaborative Self ratings & User If a resource is liked by those with Filtering ratings similar tastes, recommend it Recommendation Schemes (continued) Scheme Name Sources Rule Weak Content- Self usage & If a resource is structurally similar based Characteristics to ones previously used, recommend it. Weak Collaborative Self usage & Other If a resource is used by those with Filtering usage similar downloads, recommend it Content-based Self ratings & If a resource is similar to ones Collaborative Characteristics & previously liked and liked by those Filtering User ratings with similar tastes, recommend it. Home Run Self ratings & If a resource is similar to ones Characteristics & previously liked and liked by those User ratings & with similar tastes, and matches Profile areas of interest, recommend it. Challenges to Recommendation Schemes • How can multiple recommendation schemes be implemented? • Do more specific schemes work better than less specific ones? • How should the choice of schemes be conditioned by data source quality and quantity (costs and benefits)? • How should multiple purposes be factored into recommendations? Towards Solutions to Challenges • Do natural experiments: – Evaluation, Use and Profile information will grow over time in iLumina – Test the efficacy of different schemas as data becomes richer – Devise schemes that effectively incorporate new information sources • Consider extending profiles to include multiple purposes and goals – Index user ratings and usage data by purpose References and Sources • Our project website: http://www.ilumina-project.org • Project papers on the site: – JCDL: Developing Recommendation Services for a Digital Library with Uncertain and Changing Data. Joint Conference on Digital Libraries (JCDL) – CACM: Towards a Sharable Digital Library of Reusable Teaching Resources: Roles for Rich Metadata Communications of the ACM, Special Digital Libraries Issue, April 2001 – JERIC: A Sharable Digital Library of Reusable Teaching Resources: Roles for Rich Metadata. Journal on Education Resources in Computing (JERIC). – JLibAdmin: Library Services Today and Tomorrow: Lessons from iLumina, a Digital Library for Creating and Sharing Teaching Resources. Journal of Library Administration.
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