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

Outline  Motivation and aims  Basic recommender system  Architectural extensions  Domain model  Task model  User model  Personalization model  Service registry  Calais service integration  Conclusion

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.

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

Basic Recommender System Portal Layer – aggregation of portlets using filter chains

Basic Recommender System Analysis Layer – named entity extraction using UIMA framework

Basic Recommender System Semantic Tagging Layer – wrapping the extracted entities into semantic tags

Basic Recommender System Recommendation Layer – generating a list of references to the similarly annotated information pieces

Basic Recommender System Service Integration Layer – mapping the tagged entities to the corresponding external service

Basic Recommender System Presentation Layer – invocation of the selected external services

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

Architectural Extensions  Generation of user-specific recommendations  Mechanism for flexible mapping of information types to information sources  Harnessing external unstructured information analysis engines

Extended Architecture

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

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

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

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

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

Multidimensional Representation of the Personalization Model User Interests Document Concepts User Expertise

Intersection of Dimensions GetEncyclopediaArticleGetCompanyWebsiteGetNews Bank Banking: interested Banking: novice Document Concepts User Interests User Expertise

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

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  …

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

Q uestions & A nswers