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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.
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Personalized Recommendation of Related Content Based on Automatic Metadata Extraction Andreas Nauerz1, Fedor Bakalov2, Birgitta König-Ries2, Martin Welsch1 1IBM Deutschland Research & Development GmbH, Germany 2Friedrich Schiller University of Jena, Germany www.minerva-portals.de
Outline • Motivation and aims • Basic recommender system • Architectural extensions • Domain model • Task model • User model • Personalization model • Service registry • Calais service integration • Conclusion www.minerva-portals.de
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 Motivation • In order to access these different pieces of information, the user has to launch new browser windows and direct them to appropriate resources. www.minerva-portals.de
Company Profile Stock Quotes Experts Aims • Automatically augment portal documents with recommendations to background information and related content Company X Company Y Stock Market Technology www.minerva-portals.de
Basic Recommender System Portal Layer – aggregation of portlets using filter chains www.minerva-portals.de
Basic Recommender System Analysis Layer – named entity extraction using UIMA framework www.minerva-portals.de
Basic Recommender System Semantic Tagging Layer – wrapping the extracted entities into semantic tags www.minerva-portals.de
Basic Recommender System Recommendation Layer – generating a list of references to the similarly annotated information pieces www.minerva-portals.de
Basic Recommender System Service Integration Layer – mapping the tagged entities to the corresponding external service www.minerva-portals.de
Basic Recommender System Presentation Layer – invocation of the selected external services www.minerva-portals.de
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 www.minerva-portals.de
Architectural Extensions • Generation of user-specific recommendations • Mechanism for flexible mapping of information types to information sources • Harnessing external unstructured information analysis engines www.minerva-portals.de
Extended Architecture www.minerva-portals.de
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 www.minerva-portals.de
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 Task Model www.minerva-portals.de
User Model • Reflects various user features • Static part: • Date of birth • Gender • Mother tongue • Dynamic part: • Interests • Expertise • Represented as an overlay model Overlay User Model Domain Model www.minerva-portals.de
Representation of User Interests and Expertise • Numerator – number of occurrences of concepti for userj • Denominator – total number of occurrences of all concepts registered for userj www.minerva-portals.de
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 www.minerva-portals.de
Multidimensional Representation of the Personalization Model User Interests User Expertise Document Concepts www.minerva-portals.de
Intersection of Dimensions User Interests User Expertise Banking: novice GetEncyclopediaArticle GetCompanyWebsite GetNews Banking: interested Document Concepts Bank www.minerva-portals.de
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 www.minerva-portals.de
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 • … www.minerva-portals.de
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 www.minerva-portals.de
Questions Answers & www.minerva-portals.de
