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Generic User Modeling Systems Personalizing the User Experience Emre Göynügür Advisor: Matthieu Deru

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Page 1: Generic User Modeling Systems - DFKImfeld/PUX12/Slides_Goynugur.pdf · • User Modeling – HCI – Customization and Adaptation – Amazon, Google, Ebay, Facebook ... • Serves

Generic User Modeling Systems

Personalizing the User Experience

Emre Göynügür Advisor: Matthieu Deru

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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!

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Introduction (Stereotype)

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Stereotype Example: Users

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Outline

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•  Introduction •  GUM Systems •  UM Trends •  Novel Approach (TUMS) •  Conclusion

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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

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User Modeling Shell

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•  Part of an application system •  Software component

– Maintainable –  Reusable

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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

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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

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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

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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

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Conclusion

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•  User Modeling – User models, stereotypes

•  GUM Systems •  Smart Appliances of UM

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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