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| Date post: | 28-Dec-2015 |
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User Profiling: Areas
Information Retrieval Personalised Search Personalised TV Listings Recommendation Systems Expert Finding Systems – profile matching
Information Filtering Spam Filters News Filtering
E-Learning Learner Profiles
Intelligent Environments Intelligent Agents Behaviour Prediction
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User Profiling
A user profile can be used like a filter on a set of data, with various sets of data:
Search Engine results Environment variables such as lighting settings and temperature
settings Recipes News feeds… etc. … any collection of data items that could be personalised
Any information available about the user can be incorporated into the profile
Likes, dislikes – specified or implied Various histories e.g. past behaviour, purchasing, browsing, TV
watching, bookmarks Disabilities and/or medical details Future data sources
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User Profiling: Steps
Raw Data
Processing Method/Algorithm
Storage and Representation
User Profile
Application
Sources of Data
Collect Relevant Data
Decide what source to use.
Go get it
Got it!
Get some meaning out of it.
Save it in a consistent format or structure.
Call it a User Profile.
Use it.
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Building a User Profile
Various Data Source ApplicationsExamples of Data Sources
HTML file (e.g. bookmarks) XML files Text files (e.g. rules)
Various Methods of ProcessingVarious Representations of ProfilesSome Example Personalised Applications
and Data Sources…
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Data Source Applications
Search Engine
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Data Source Applications
Question Answering
System
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Data Source Applications
Personalised TV
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Data Source Applications
Intelligent Environment
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Data Sources - XML Recipe
Ref: Recipe example from http://www.brics.dk/~amoeller/XML/xml/example.html
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Data Sources – Fuzzy Logic Example
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Methods of processing data
HITS Algorithm
Rocchio Algorithm
Text Filtering
Processing Method/
Algorithm
Naive Bayesian
Classification
Generalisation
ThesauriWord-Sense
Disambiguation
Term to Concept Mapping
Perceptron
Categorisation
Similarity Comparison
Text Mining
Genetic Algorithms
From raw data to profile representation...
Link Analysis
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Representations of Profiles
Ontology Vector(s)
RepresentationKeywords
Bag of Words
A user profile can be represented as...
Concept Hierarchy
Matrix
Rules
Linear Model
Prototype
Boolean
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A Global, Unified Profile
Potentially, one single profile could be used anywhere, for any application.
Currently, the common theme in previous research, is that there is no common theme! Different data storage methods, data processing
methods and algorithms, representation of profiles etc.
What is the most efficient of these different methods and processes?
Can a user profile from one application be used within another application?
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A Global, Unified Profile
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A Global, Unified Profile
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Global Profile - Considerations
Mapping and categorising items to the Global Profile E.g. a generic term for temperature, heating, radiators
etc. Extensible way to add new data (and data sources) to
the profile Textual data Fuzzy data Future data items and sources – e.g. SatNav
Data storage choices Main Server Distributed
Transparency of the profile Updating and synchronising
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Security, Privacy and Legal Implications
The User must be in ultimate control! What data should be used in a profile? Purchasing
history? Criminal record? Who and what should be allowed access to a profile?
The Police? The Government? Could it be used against their wishes?
Fine balance between what is good-intentioned personalisation and what is a complete loss of privacy
As people lose more and more control of what information is stored about them, their personal freedom may feel encroached upon, resulting in a strong resistance to further developments towards user profiling
