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Generic User Modeling Systems
Personalizing the User Experience
Emre Göynügür Advisor: Matthieu Deru
Introduction
• User Modeling – HCI – Customization and Adaptation – Amazon, Google, Ebay, Facebook …
• Generic User Modeling Systems – User Modeling Shell Systems – User Modeling Servers
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Amazon
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Amazon (cont’d)
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Introduction (User Model)
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• User Model – Goal – Knowledge – Static, Dynamic, Stereotype
based
• Black: Insufficient knowledge • Nested: Conflict!
Introduction (Stereotype)
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Stereotype Example: Users
Outline
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• Introduction • GUM Systems • UM Trends • Novel Approach (TUMS) • Conclusion
Outline (GUM Systems)
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• GUM Systems – User Modeling Shells
» Historical Development » Examples
– User Modeling Servers » Characteristics » Issues » Requirements » Examples
User Modeling Shell
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• Part of an application system • Software component
– Maintainable – Reusable
Historical Development
• Inherited from User Adaptive Systems • General User Modeling System ’86 • Term: “User Modeling Shell”
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UM Shell Examples
• UMT (User Modeling Technology) – Hierarchical stereotypes
• PROTUM (A Prolog based tool for UM) – UM is a list of constants – More sophisticated retraction than UMT
• TAGUS – Diagnosis of unexpected behavior
• um
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um
• Toolkit for user modeling • Stores attribute-value pairs • Stores evidence and its source • Infers from the evidence
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Outline (GUM Systems)
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• GUM Systems – User Modeling Shells
» Historical Development » Examples
– User Modeling Servers » Characteristics » Issues » Requirements » Examples
User Modeling Servers
• Similar to user modeling shells • Serves many applications • Not part of an application system
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Characteristics
• Information maintained in a repository • Information is not redundant • Information is gathered from many app.s • Methods for security can be applied (e.g. encryption)
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Issues
• Design: Central vs Distributed • Performance • Scalability • Availability
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Required Services
• Assumptions about types of user characteristics • Common characteristics of sub-groups • Classification of users • Recording user behavior
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Required Characteristics
• Support for quick adaptation • Extensibility • Import user-related information • Management of distributed information
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Required Characteristics (cont’d)
• Support for open standards • Scalability • Privacy • Failover strategies • Transactional consistency
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Academic Examples
• BGP-MS • DOPPELGANGER • CUMULATE • PERSONIS
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BGP-MS
• Represents assumptions in first-order predicate logic • Subset of assumptions stored in terminological logic • Inferences among different types • Can be used as a nw server with multi-user
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BGP-MS Internal View
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BGP-MS Communication
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DOPPELGANGER
• Collects information from HW and SW • Extrapolates the collected data
– Linear prediction – Markov models – Clustering
• Users can inspect and modify
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Commercial Examples
• Group Lens • ATG Adaptive Scenario Engine • enQuire Identity Server
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Group Lens
• Applies collaborative filtering algorithms • Stores user ratings in database • Stores a correlation matrix in main memory • Collects data from forms and by monitoring
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ATG Adaptive Scenario Engine
• Users are assigned to groups – Demographics, system usage, SW, HW, NW
• Inferring rules can be defined • External data can be integrated
– SQL, XML, and Web Services • Acquired by ORACLE
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Why ORACLE acquired ATG?
• Unified CRM – Same brand experience with
» Mobile, online, in store channels etc.. – Personalized experience
• ORACLE – Data management
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ORACLE - ATG
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enQuire Identity Server
• Multi-functional server – Security policies, access control – Federates and filters information
• Embedded virtual directory engine – LDAP, ODBC or an API
• Static and dynamic binding
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Outline
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• Introduction • GUM Systems • UM Trends • Novel Approach (TUMS) • Conclusion
User Modeling Trends
• Mobile User Models • User Models for Smart Appliances • Agent-Based User Modeling Systems • Multiple-Purpose Usage • Diverse GUM Systems
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Mobile User Models
• Increasing usage of mobile devices • HCI in a sensor equipped environment • Limited computation power • Limited bandwidth • Example: Mobile recommender systems
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UM for Smart Appliance Examples
• Car radios • Electronic car keys • Ford Microsoft Sync
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Microsoft – Ford Sync
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Pandora (Radio Recommender)
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Agent-Based UM Systems
• Automation of UM servers – Ubiquitious computing – Communities of computer users
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Multiple-Purpose Usage
• Organizational directory services • Skill inventory systems • Global expert-finding
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Outline
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• Introduction • GUM Systems • UM Trends • Novel Approach (TUMS) • Conclusion
TUMS: Twitter-based User Modeling Service
• Public web application • Topic detection • Entity extraction • Structured
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TUMS (cont’d)
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Overview of a User Profile
Conclusion
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• User Modeling – User models, stereotypes
• GUM Systems • Smart Appliances of UM
Questions
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References
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1. Domik, G.: User Modelling in Human Computer Interaction 2. http://en.wikipedia.org/wiki/User_modeling 3. http://www.oracle.com/us/corporate/Acquisitions/atg/index.html 4. Kay, J.: The um toolkit for cooperative user modelling. 5. Kobsa, A.: Generic User Modeling Systems. 6. Kobsa, A.; Pohl, W.: The User Modeling Shell System BGP-MS. 7. Tao, K.; Abel, F.; Gao, Q.; Houben, G.: TUMS: Twitter-based User
Modeling Service