1 personalized hypermedia presentation techniques for improving online customer relationships kobsa,...
TRANSCRIPT
1
Personalized hypermedia presentation techniques for improving online customer relationships
Kobsa, Koenemann, and Pohl
Presenters: Stacy Tang and Matt Yeh
2
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
3
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
4
Introduction:Why personalization?
• providing value to customer
• Brick and Mortar: – personal service– tailored products
5
Introduction:Why use the web for personalization?
• Collect large amount of data
• Rapid updates
• World-wide and 24/7
• Dynamic creation of content
6
Introduction:Why personalize on the web?
• # page views
• length of page views
• # new customers
• # visitors
• revenue
7
Introduction:How the Internet fits in
Sales Cycle
Pre During
Post
Establish and strengthen brand
Online ordering and purchasing
Reassure customer and product support
8
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.
9
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
10
Input Data:User data
• Information about personal characteristics of the user:– Demographic– Knowledge– Skills and capabilities– Interests and preference– Goals and plans
11
Input Data:User data - demographics
Objective facts:
• record
• geographic
• characteristics
• lifestyle
• registration
12
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
13
Input Data:User data - user knowledge ex
14
Input Data:User data - skills & capabilities
• skills - “knowing how”; actions that the user is familiar with
• capabilities - actions that user is able to perform
15
Input Data:User data - interests and preferences
• Align content with user interests
• Important in recommendation systems
16
Input Data:User data - goals and plans
• Plan-recognition
• Facilitate interaction
17
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
18
Input Data:Observable usage - selective actions
• Clicking on a link as an indicator for:– interest (+ only)– unfamiliarity (+ only)– preference
19
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
20
Input Data:Usage data - finding regularities
• Process usage data to find:
– Frequency
– Situation-based correlations
– Action sequences
21
Input Data:Environment data• Software
– browser, platform– plug-ins– Java and Javascript
• Hardware– bandwidth– processing speed– display– input
• Locale– location– characteristics of location
22
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
23
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
24
Acquisition Methods:User Model Acquisition Methods
• Strategies for obtaining data about user characteristics– Active methods– Passive Methods
25
Acquisition Methods: User Supplied Information
• Obvious strategy is to have user supply info– Initial Interview– Registration Process
• Examples– Soccernet.com– My.Yahoo.com
26
27
28
29
Acquisition Methods:Problems with Interviews
• Self-assessment may be error-prone
• Solution: “Indirect” assessment
30
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
31
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
32
Acquisition Methods: Problems with Interviews, cont.
–Solutions to this problem:• Let the user initiate setup
• Fold setup into interaction gradually
• Automate setup
33
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”
34
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
35
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.
36
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
37
Acquisition Methods:Plan Recognition
• Microsoft XP monitors the applications the user most frequently uses
38
Acquisition Methods: Stereotype Reasoning
• “Stereotype reasoning” for hypermedia is a method that works like everyday stereotyping
39
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
40
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
41
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
42
• 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
43
Acquisition Methods: Environmental Data
44
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
45
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
46
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
47
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”
48
Representation and Secondary Inference
• Some systems have higher demands
• Need to represent information to make inferences based on initial acquisition results– “Secondary Inferences”
49
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
50
Representation & Secondary Inference: Logic-based approach: Concept Hierarchy
thing
fish
shark
mammal
whale
orcahumpback
51
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”
52
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”
53
Representation & Secondary Inference: Interest Profile
• Representation of user’s general preference or affinity for object based on features of object
• Example: Movies
Action Movies
54
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
55
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
56
Representation & Secondary Inference: Analogical Reasoning
• Reasoning based on similarity of users
• One technique is Clique-based filtering
57
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
58
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
59
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
60
Adaptation Production:Adaptation of content
• Functions of adapting content:– optional explanation– optional detailed information– personalized recommendations– optional opportunistic hints
61
Adaptation Production:Adaptation of content
• Techniques of adapting content:– page variants– fragment variants– fragment coloring– adaptive stretchtext– adaptive natural-language generation
62
Adaptation Production:Adaptation of content example
CNN.com: page variant
63
Adaptation Production:Adaptation of content example
MyYahoo: fragment variant
64
Adaptation Production:Adapt presentation and modality
• Change of format and layout
• Change of modality
65
Adaptation Production:Adapt presentation example
MyYahoo: personalize format
66
Adaptation Production:Adapt presentation example
MyYahoo: personalize layout
67
Adaptation Production:Adapt modality example
Map
directions
Text only
directions
68
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
69
Adaptation Production:Adaptation of structure
• Techniques for structure adaptation– link sorting– link annotation– link hiding and “unhiding”– link disabling and enabling– link removal/addition
70
Adaptation Production:Adaptation of structure example
Link annotation
Link sorting
71
Adaptation Production:Adaptation of structure example
Recommend products with link additions
72
Outline
• Introduction
• Input data
• Acquisition methods
• Representation and secondary inferences
• Adaptation production
• Conclusion
73
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
74
Conclusions & Prospects: Personalization Applications
• Nordstrom.com includes a link showing user how to contact a sales expert
75
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
76
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
77
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