1 personalized hypermedia presentation techniques for improving online customer relationships kobsa,...
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Personalized hypermedia presentation techniques for improving online customer relationships
Kobsa, Koenemann, and Pohl
Presenters: Stacy Tang and Matt Yeh
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Introduction:Why personalization?
• providing value to customer
• Brick and Mortar: – personal service– tailored products
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Introduction:Why use the web for personalization?
• Collect large amount of data
• Rapid updates
• World-wide and 24/7
• Dynamic creation of content
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Introduction:Why personalize on the web?
• # page views
• length of page views
• # new customers
• # visitors
• revenue
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Introduction:How the Internet fits in
Sales Cycle
Pre During
Post
Establish and strengthen brand
Online ordering and purchasing
Reassure customer and product support
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Introduction: Definition
Personalized Hypermedia Application:
An interactive system that allows users to navigate a network of linked hypermedia objects (i.e. web pages) and adapts the content structure and/or presentation of the networked hypermedia objects to each individual user’s characteristics, usage behavior and/or usage environment.
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Input Data:User data
• Information about personal characteristics of the user:– Demographic– Knowledge– Skills and capabilities– Interests and preference– Goals and plans
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Input Data:User data - demographics
Objective facts:
• record
• geographic
• characteristics
• lifestyle
• registration
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Input Data:User data - user knowledge
• “knowing what”
• Adjust the presentation based on user knowledge– expert not bored by unnecessary details– novice not confused by details they don’t understand
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Input Data:User data - user knowledge ex
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Input Data:User data - skills & capabilities
• skills - “knowing how”; actions that the user is familiar with
• capabilities - actions that user is able to perform
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Input Data:User data - interests and preferences
• Align content with user interests
• Important in recommendation systems
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Input Data:User data - goals and plans
• Plan-recognition
• Facilitate interaction
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Input Data:Usage data
• Directly observed– ways users interact with a system– can directly lead to adaptation
• General regularities– further process the above to deduce information about
the user
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Input Data:Observable usage - selective actions
• Clicking on a link as an indicator for:– interest (+ only)– unfamiliarity (+ only)– preference
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Input Data:Observable usage - other interactions
PositiveIndicator
NegativeIndicator
Temporalviewing behavior
Rating
Purchase &related
Confirmatory/disconfirmatory
Viewing time of page
Explicit ratings (i.e., Amazon)
putting items in shopping cart
usage and indicator for user interest
Save document,print document,
bookmarking page, forward story by email
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Input Data:Usage data - finding regularities
• Process usage data to find:
– Frequency
– Situation-based correlations
– Action sequences
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Input Data:Environment data• Software
– browser, platform– plug-ins– Java and Javascript
• Hardware– bandwidth– processing speed– display– input
• Locale– location– characteristics of location
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Acquisition Methods: User Acquisition Methods
• Methods to obtain data that can be input into personalized hypermedia – User information -> “user model”– Usage Information -> “usage model”– Environment
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Acquisition Methods:User Model Acquisition Methods
• Strategies for obtaining data about user characteristics– Active methods– Passive Methods
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Acquisition Methods: User Supplied Information
• Obvious strategy is to have user supply info– Initial Interview– Registration Process
• Examples– Soccernet.com– My.Yahoo.com
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Acquisition Methods:Problems with Interviews
• Self-assessment may be error-prone
• Solution: “Indirect” assessment
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Acquisition Methods: Indirect Assessment
• Website where we want to acquire a user characteristic
– User’s expertise in speaking English
• We can ask user • Better method may be to
determine expertise indirectly
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Acquisition Methods: Problems with Interviews, cont.
• “Paradox of the active user”– User anxious to begin
immediate task and are too busy for setup
– Doing setup may save user time later
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Acquisition Methods: Problems with Interviews, cont.
–Solutions to this problem:• Let the user initiate setup
• Fold setup into interaction gradually
• Automate setup
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Acquisition Methods Passive Acquisition
• Acquisition where interaction is not initiated with user
• Less disturbing or annoying
• Passive Acquisition Methods– “Acquisition rules”– “Plan Recognition”– “Stereotype Reasoning”
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Acquisition Methods: Acquisition Rules
• Heuristics or Inference rules– Generate assumptions about user given available
information– Example: If user wants to know concept X, we
assume that user does not know concept X
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Acquisition Methods: Acquisition Rules
– Example: We want to know the user’s level of experience with a program
– We can accomplish this with inference rules based on knowledge of when the user last used the program
If the user has been away too long:Downgrade experience level by 1.
If the user has used the system long enough since the last update:Upgrade experience level by 1.
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Acquisition Methods:Plan Recognition
• Reasoning about user goals & action sequences user performs to achieve them
• Monitor user action to ID user plan/goal
• Modify our program to help user efficiently achieve those goals
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Acquisition Methods:Plan Recognition
• Microsoft XP monitors the applications the user most frequently uses
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Acquisition Methods: Stereotype Reasoning
• “Stereotype reasoning” for hypermedia is a method that works like everyday stereotyping
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Acquisition Methods: Stereotype Reasoning
• We create categories of users and maintain a body of info true for users in each category
• We have “triggers” for assigning users to categories• Then we can make assumptions about user based on
category membership• Example: Searching for info about childcare activates a
parent stereotype that we use to make predictions about user characteristics
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Acquisition Methods: Usage Acquisition Methods
• Acquiring usage info seems to be an easier task– We observe and record what the user does
• Simply observing user behavior may not be enough
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Acquisition Methods: Usage Acquisition Methods
– Often we want to know the context in which a user performs particular actions
– We can then use machine learning strategies to predict user actions given a certain scenario
– Situation/Action learning
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• We may want to know about context in which
user interacts with a system
• Software Information– Browser type
• Affects how hypermedia appears
– Determine browser type through header of http request
– Special programs to determine browsers type
Acquisition Methods:Environmental Data
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Acquisition Methods: Environmental Data
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Acquisition Methods: Environmental Data
• Bandwidth– Difficult to detect
• Special software to predict download times
• Prediction can be used to adapt page composition
• Hardware– Difficult to get
– Can sometimes assume from browser
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Acquisition Methods: Environmental Data
• User location– Often we want to tailor hypermedia based on user
location– Consider navigation system– For such mobile devices
• Electromagnetic fields (GPS, Bluetooth, radio, etc.)• Ultrasound• IR and optical recognition
– For stationary networked devices location is often stored in a database
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Representation and Secondary Inference
• Store user information in a way that is useful
• Can use simple methods:– Example: Maintain a list of feature-value pairs like
“CONCEPT X KNOWN” / “CONCEPT X NOT KNOWN”
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Representation and Secondary Inference
• Some systems have higher demands
• Need to represent information to make inferences based on initial acquisition results– “Secondary Inferences”
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Representation & Secondary Inference:
Deductive Reasoning Strategies
• Use a system based on logic to represent info and make inferences
• Logic-based formalisms– Propositional logic– Modal logic
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Representation & Secondary Inference: Logic-based approach: Concept Hierarchy
thing
fish
shark
mammal
whale
orcahumpback
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Representation & Secondary Inference: Logic-Based approaches
• Shortcomings– Method is rigid. Does not deal with changes to user
model well– Does not deal with uncertainty well
• May not be sure contents of user/usage model are accurate
• For example, we might be 60% sure that the user knows that “a shark is a fish”
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Representation & Secondary Inference: Inductive Reasoning: Learning
• In previous examples, we wanted to draw specific assumptions about users
• Use specific observations to draw general conclusions
• In domain of customer relation management we are most often concerned with creating a general “interest profile”
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Representation & Secondary Inference: Interest Profile
• Representation of user’s general preference or affinity for object based on features of object
• Example: Movies
Action Movies
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Representation & Secondary Inference: Techniques to Acquire an Interest Profile• Machine Learning techniques• Neural Networks• Example: Neural net to assemble
a interest profile about websites– Create network based on
features of website– Train network with the a user’s
ratings of websites– Network "stores" the interest
profile– Network will predict if a new
website will be interesting to the user
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Representation & Secondary Inference: Problems with feature-driven inductive approaches
• Not easy to parse out features of some objects (e.g. multimedia objects)
• Training period (as in neural net example) may not be possible.
• Interest in object may not depend on features
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Representation & Secondary Inference: Analogical Reasoning
• Reasoning based on similarity of users
• One technique is Clique-based filtering
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Representation & Secondary Inference: Clique-based Filtering
• Adapt to the individual user based on the behavior of similar users– “Interest neighbors”
• The set of similar users constitute an implicit profile of user
• Make predictions based on implicit profile
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Representation & Secondary Inference: Clique-based filtering
• Example: Amazon.com looks for users who have made similar purchases and makes predictions about other products you may like
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Adaptation Production:Adaptation of content
• Functions of adapting content:– optional explanation– optional detailed information– personalized recommendations– optional opportunistic hints
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Adaptation Production:Adaptation of content
• Techniques of adapting content:– page variants– fragment variants– fragment coloring– adaptive stretchtext– adaptive natural-language generation
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Adaptation Production:Adaptation of content example
CNN.com: page variant
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Adaptation Production:Adaptation of content example
MyYahoo: fragment variant
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Adaptation Production:Adapt presentation and modality
• Change of format and layout
• Change of modality
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Adaptation Production:Adapt presentation example
MyYahoo: personalize format
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Adaptation Production:Adapt presentation example
MyYahoo: personalize layout
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Adaptation Production:Adapt modality example
Map
directions
Text only
directions
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Adaptation Production:Adaptation of structure
• Functions of structure adaptation– recommendations
• products, information, and navigation
– orientation and guidance• personalized overview maps, guided site tours
– personal views and spaces• bookmarks
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Adaptation Production:Adaptation of structure
• Techniques for structure adaptation– link sorting– link annotation– link hiding and “unhiding”– link disabling and enabling– link removal/addition
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Adaptation Production:Adaptation of structure example
Link annotation
Link sorting
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Adaptation Production:Adaptation of structure example
Recommend products with link additions
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Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
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Conclusions & Prospects:Personalization applications
• Where will personalization be used?• Public websites
– keeping visitors– turning visitors into customers– making visitors return
• Personalization not always needed, and will not make human sales people obsolete
• Websites where customers can ask for human assistance can be effective
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Conclusions & Prospects: Personalization Applications
• Nordstrom.com includes a link showing user how to contact a sales expert
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Conclusions & Prospects:Personalization Applications
• "Walk-up and use" kiosks found in fairs, exhibitions, showrooms
• Mobile Devices– Phones– PDA’s– Car-mounted Devices
• Universal Access systems– Hypermedia personalized to
meet needs of special users– E.g., those with disabilities
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Conclusions & Prospects: Recommendations for Personalization
• Remember the "Paradox of the active user“
• Avoid lengthy registration process
• Expose user to content immediately
• Offer adaptation as an option
• Allow user to correct or undo adaptations
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Conclusions & Prospects: Recommendations, cont.
• Log user navigation at page level• critical in site design
• Logging and personal info acquisition in general leads to privacy concerns– must be addressed proactively– tell user what is being done with personal info
• tell them how providing personal info improves user experience
• if possible, allow user to opt out of logging
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