1. www.minerva-portals.de1 Personalized Recommendation of Related Content Based on Automatic Metadata Extraction Andreas Nauerz 1, Fedor Bakalov 2, Birgitta König-Ries 2, Martin Welsch 1 1 IBM Deutschland Research & Development GmbH, Germany 2 Friedrich Schiller University of Jena, Germany

2. www.minerva-portals.de2 Outline  Motivation and aims  Basic recommender system  Architectural extensions  Domain model  Task model  User model  Personalization model  Service registry  Calais service integration  Conclusion

3. www.minerva-portals.de3 Motivation  In order to take right decisions, users need access to additional sources of background information and related content.  The required additional information might be stored in various places, e.g. wikies, financial databases, company directories, etc. Company Profile Stock Quotes Experts  In order to access these different pieces of information, the user has to launch new browser windows and direct them to appropriate resources.

4. www.minerva-portals.de4 Aims  Automatically augment portal documents with recommendations to background information and related content Stock Quotes Company X Company Y Stock Market Technology Company Profile Experts

5. www.minerva-portals.de5 Basic Recommender System Portal Layer – aggregation of portlets using filter chains

6. www.minerva-portals.de6 Basic Recommender System Analysis Layer – named entity extraction using UIMA framework

7. www.minerva-portals.de7 Basic Recommender System Semantic Tagging Layer – wrapping the extracted entities into semantic tags

8. www.minerva-portals.de8 Basic Recommender System Recommendation Layer – generating a list of references to the similarly annotated information pieces

9. www.minerva-portals.de9 Basic Recommender System Service Integration Layer – mapping the tagged entities to the corresponding external service

10. www.minerva-portals.de10 Basic Recommender System Presentation Layer – invocation of the selected external services

11. www.minerva-portals.de11 Limitations  Large number of irrelevant recommendations  Hardcoded binding of information types to sources of related content  Huge amount of work required to develop analysis engines

12. www.minerva-portals.de12 Architectural Extensions  Generation of user-specific recommendations  Mechanism for flexible mapping of information types to information sources  Harnessing external unstructured information analysis engines

13. www.minerva-portals.de13 Extended Architecture

14. www.minerva-portals.de14 Finance Domain Model  Defines general and finance- related concepts  Reuses concepts from LSDIS Finance Ontology and XBRL Ontology  Grounded on the Proton Upper Level Module  Defines fine-grained categorization of industry sectors (partially based on the Yahoo Taxonomy)  Represented as an OWL ontology

15. www.minerva-portals.de15 Task Model  Defines information- gathering actions that users might want to take on the portal  Two types of actions:  generic actions – can be used across different domains, e.g. GetEncyclopediaArticle  domain-specific actions – applicable only in a specific domain, e.g. GetStockQuotes  Actions are represented as ontological concepts and described by their input and output parameters

16. www.minerva-portals.de16 User Model  Reflects various user features  Static part:  Date of birth  Gender  Mother tongue  Dynamic part:  Interests  Expertise  Represented as an overlay model Domain Model Overlay User Model

17. www.minerva-portals.de17 Representation of User Interests and Expertise  Numerator – number of occurrences of concept i for user j  Denominator – total number of occurrences of all concepts registered for user j

18. www.minerva-portals.de18 Personalization Model  Specifies personalization rules that govern what content is provided to the user  Personalization rule is represented in the ECA form: on (event) if (condition) then (actions)  Event denotes a situation when the user encounters a certain concept in the text  Condition is a combination of user features and context descriptors  Actions define the information gathering actions that should be delivered to the user if the event occurs

19. www.minerva-portals.de19 Multidimensional Representation of the Personalization Model User Interests Document Concepts User Expertise

20. www.minerva-portals.de20 Intersection of Dimensions GetEncyclopediaArticleGetCompanyWebsiteGetNews Bank Banking: interested Banking: novice Document Concepts User Interests User Expertise

21. www.minerva-portals.de21 Service Registry  Central database for storing information about internal and external services  The registry maps each action from the Task Model to the service that “does” the action  e.g. getEncyclopdediaArticle -> Wikipedia  Services are provided with WSDL description

22. www.minerva-portals.de22 Calais Service  Ingests unstructured text and returns semantically annotated document in RDF format  Supports extraction of business entities, events, and facts  Entities (total: 38)  Currency  Industry term  Organization  Person  …  Events and facts (total: 38)  Acquisition  Alliance  Bankruptcy  Merger  …

23. www.minerva-portals.de23 Conclusion  Augmenting portal documents with automatically generated recommendations to background information and related content  Extension of our previous recommender system:  User-specific recommendations  Flexible mapping of information types to services  Leveraging external analysis engines for tagging  The extensions are currently being incorporated in the existing recommender system prototypically implemented in IBM’s WebSphere Portal

24. www.minerva-portals.de24 Q uestions & A nswers