1. Web-Technology
Lecture 14

2. Pre-quiz What does it mean - “lost in hyperspace”?
What are the three stages of Adaptive Search?
What are the two main categories of Adaptive Hypermedia technologies?
Why Amazon wants you to rate products?

3. Adaptive Web

4. Size of the Web - Content
# of Web-hosts: > 1 billion
# of Web-pages: > 55 billion
80% of Web-content generated by users:
Daily:
500 Million Tweets
4 Million Hours of YouTube videos
3.6 Billion Instagram Likes
4.3 BILLION Facebook messages
Many modern Web systems suffer from an inability to be “all things to all people”. Web courses present the same static learning material to students with widely differing knowledge of the subject. Web stores offer the same selection of "featured items" to customers with different needs and preferences. Health information sites present the same information to readers with different health problems. The systems that can’t meet the needs of their heterogeneous users are often compared to a department store that offers one size of clothing to all customers. A remedy for the negative effects of this "one-size-fits-all'' approach is to develop systems that are able to adapt their behavior to the goals, tasks, interests, and other features of individual users and groups of users. Enter the Adaptive Web – a new research area on the crossroads of human- computer interaction and artificial intelligence. Starting with a few pioneering works on adaptive hypertext in early 1990, it now attracts many researchers from different communities such as hypertext, user modeling, machine learning, natural language generation, information retrieval, intelligent tutoring systems, and cognitive science. Currently, the established application areas of adaptive Web systems are education, information retrieval, and kiosk-style information systems. A number of more recent projects are also exploring new application areas such as e-commerce, medicine, and tourism.

5. Size of the Web - Users
Many modern Web systems suffer from an inability to be “all things to all people”. Web courses present the same static learning material to students with widely differing knowledge of the subject. Web stores offer the same selection of "featured items" to customers with different needs and preferences. Health information sites present the same information to readers with different health problems. The systems that can’t meet the needs of their heterogeneous users are often compared to a department store that offers one size of clothing to all customers. A remedy for the negative effects of this "one-size-fits-all'' approach is to develop systems that are able to adapt their behavior to the goals, tasks, interests, and other features of individual users and groups of users. Enter the Adaptive Web – a new research area on the crossroads of human- computer interaction and artificial intelligence. Starting with a few pioneering works on adaptive hypertext in early 1990, it now attracts many researchers from different communities such as hypertext, user modeling, machine learning, natural language generation, information retrieval, intelligent tutoring systems, and cognitive science. Currently, the established application areas of adaptive Web systems are education, information retrieval, and kiosk-style information systems. A number of more recent projects are also exploring new application areas such as e-commerce, medicine, and tourism.

6. Size of Web - Activities
Searching and Browsing
Reading, listening, watching
Shopping and banking
Communicating and exchanging information
Working and having fun

7. The big question Does one size fit all?
Many information systems suffer from an inability to tailor for “all people”.
Web courses present the same static material to all students with differing knowledge.
Web stores offer the same selection of "featured items" to customers with different needs.
Health information sites present the same information to readers with different health problems.
The systems that can’t meet the needs of their heterogeneous users are often compared to a department store that offers one size of clothing to all customers. A remedy for the negative effects of this "one-size-fits-all'' approach is to develop systems that are able to adapt their behavior to the goals, tasks, interests, and other features of individual users and groups of users. Enter the Adaptive Web – a new research area on the crossroads of human-computer interaction and artificial intelligence. Starting with a few pioneering works on adaptive hypertext in early 1990, it now attracts many researchers from different communities such as hypertext, user modeling, machine learning, natural language generation, information retrieval, intelligent tutoring systems, and cognitive science. Currently, the established application areas of adaptive Web systems are education, information retrieval, and kiosk-style information systems. A number of more recent projects are also exploring new application areas such as e-commerce, medicine, and tourism.

8. An alternative: Adaptive systems
Adaptive information systems attempt to treat different users differently
Adaptive Web:
combines principles of
AI (formal models of user, task, domain, …) and
HCI (interactive interfaces with dynamically personalized components)
builds systems that provide personalized experience by supporting users in their online information tasks
Web
Collects information about individual user
User Modeling side
Adaptive
User Model
System
Adaptation side
Provides adaptation effect
Classic loop “user modeling - adaptation” in adaptive systems

9. What can be modelled by a user model?
Knowledge about the content and the system
Short-term and long-term goals
Interests and needs
Navigation / action history
User category, background, profession, language, capabilities
Platform, bandwidth, context…
… the exact content of user model is task-dependent

10. What Can be Adapted? Adaptive Search Systems
tailor search results
Adaptive recommender systems
Suggest new information items
Adaptive Hypermedia Systems
adaptive presentation
adaptive navigation support
Adaptive news systems
Filter out irrelevant news or reorder news feeds
Adaptive GUI
menu adaptation
dialog form adaptation
Intelligent Tutoring Systems
adaptive course sequencing
adaptive group formation
...

11. What personalization can help solving
Information overload
occurs when the amount of input to a system exceeds its processing capacity. Decision makers have limited cognitive processing capacity. Consequently, information overload causes reduction in decision quality.
Lost in a hyperspace
A phenomenon of disorientation experienced by users reading and navigating hypermedia documents
Getting right information at the right time
Different users are different
Same users are different at different times
There is no silver bullet adaptive technology
Adaptation itself is not a silver bullet technology

12. Personalized Information Access
Adaptive search (IR, from 1980)
Use word-level profile of interests and remedial feedback to adapt search and result presentation
Adaptive hypermedia (HT, ITS, from 1990)
Use explicit domain models and manual indexing to deliver a range of adaptation effects to different aspects of user models
Web recommenders (AI, ML, from 1995)
Use explicit and implicit interest indicators, apply clickstream analysis/log mining to recommend best resources for detected user interests
Content-based recommenders
Collaborative recommenders
Hybrid recommenders

13. Adaptive search

14. Why Search Personalization?
Different users need different documents in response to the same query
Relevance is not enough if the volume of data is high
With the growth of information even a good query can return thousands of "relevant" documents
Personalization is an attempt to find most relevant documents using information about user's goals, knowledge, preferences, navigation history, etc.

15. Adaptive Search How the search process can be adapted to the user?
How we can model the user in adaptive search?
Which adaptation technologies can be applied?

16. How can Search be Adapted?
Results
Query
Search Engine
User profile
User profile
User profile
Before search
During search
After search

17. Modeling Users in Adaptive Search
Most essential feature: user interests
Observing user document selection, adaptive IR systems build profile of user interests
Keyword-level modeling
A long list of keywords (terms) in place of a domain model
User interests are modeled as weighted vector or terms
More advanced systems use several profiles for different domains or timeframes

18. Keyword-based User Profiles

19. Before: Query Expansion
User profile is applied to add terms to the query
Popular terms could be added to introduce context
Similar terms could be added to resolve indexer- user mismatch
Related terms could be added to resolve ambiguity
Works with any IR model or search engine

20. During
The user profile is used to organize the results of the retrieval process
Retrieve the most interesting documents
Filter out irrelevant documents
In this case the use of the profile adds an extra step to processing
Extended profile can be used effectively
Demographics
Location
Social circle

21. After
Re-ranking of search results is a typical approach for post-filtering
Each document is rated according to its relevance (similarity) to the user or group profile
This rating is fused with the relevance rating returned by the search engine
The results are ranked by fused rating
Annotation of search results
Results are provided with visual cues encoding useful information
Adaptive navigation appraoch

22. Google adaptive search
Milestones:
March, 2004 – beta
April, 2005 – non-beta extra service
November, 2011 – part of normal search
October, 2009 – social search added
Authenticated users – Google user profile
Non-authenticated users –
User profile contains:
Location
Search History (queries and clicks)
Web History (Google tracking scripts from adSense and adWords, Chrome, Android,….)
Social Networks
History from a Multitude of Google services
gender, age, languages
Topics

23. Personalized search experiment

24. Adaptive Hypermedia

25. Adaptive Hypermedia How hypertext and hypermedia can become adaptive?
Which adaptation technologies can be applied?
How can we model the user in adaptive hypermedia?

26. Why Adaptive Hypermedia?
Different people are different
Individuals are different at different times
"Lost in hyperspace”
We may need to make hypermedia adaptive where ..
There us a large variety of users
Same user may need a different treatment
The hyperspace is relatively large

27. What Can Be Adapted? Web-based systems = Pages + Links
Adaptive presentation
content adaptation
Adaptive navigation support
link adaptation

28. Classification of Adaptive Hypermedia techniques

29. Adaptive Stretchtext (PUSH)

30. Adaptive annotation in InterBook3 2 √ 1
1. State of concepts (unknown, known, ..., learned) 2. State of current section (ready, not ready, nothing new) 3. States of sections behind the links (as above + visited)

31. QuizGuide: Dual Adaptive Annotations

32. User Modeling in Classic AH
Classic AH use external models (besides user model)
Domain models, adaptation (pedagogical) modes, stereotype hierarchy, etc.
Users are modeled in relation to these models
User knowledge of loops is high
User is interested in 19th century architecture styles
Resources are connected (indexed) with elements of these models (aka knowledge behind pages)
This section presents while loop and increment
This page is for field-independent learners
This church is built in 1876

33. Domain Model Concept 4 Concept 1 Concept N Concept 2 Concept 5

34. Indexing of Nodes Concept 4 Concept 1 Concept n Concept 2 Concept m
External (domain) model
Hyperspace
Concept 4
Concept 1
Concept n
Concept 2
Concept m
Concept 3

35. Indexing of Fragments Fragment 1 Concept 4 Concept 1 Concept N
Concepts
Node
Fragment 1
Concept 4
Concept 1
Concept N
Concept 2
Fragment 2
Concept 5
Concept 3
Fragment K

36. Concept-Level User Model3 10
Concept N
Concept 2 7 2 4
Concept 5
Concept 3

37. AH: Known effects
Adaptive presentation helps users understand content faster and better
Adaptive navigation support reduces navigation efforts and brings the users to the right place at the right time
Altogether AH techniques can significantly improve the effectiveness of hypertext and hypermedia systems

38. Recommender systems

39. Recommender Systems “Native” adaptive information access approach
How can we model the user in recommender systems?
Which adaptation technologies can be applied?

40. Recommender Systems
Started as extension of work on adaptive information filtering
What is filtering? Search without explicit query
Started as library notification services – user provided profiles
Later considered user feedback (yes/no or ratings) to automatically improve profile
Modern Recommenders can start without user profile, constructing it by observation and user feedback
Clicking, rating, bookmarking, downloading, purchasing

41. Amazon.com Recommendations

43. Types of Recommender Systems
Collaborative Filtering Recommender System
Recommendations are generated based on the user history and the community of likeminded users
Content-based Recommender System
Recommendation generated from the content features associated with products and the ratings of the user
Case-based Recommender System
Similar to content-based recommendation. Information about items is represented as cases. The system recommends the cases that are most similar to user’s preference.
Hybrid Recommender System
Combination of two or more recommendation techniques to gain better performance with fewer of the drawbacks of any individual one

44. Recommendation Procedure
Understand and model users
Collect candidate items to recommend
Based on the recommendation method, predict target users’ preferences for each candidate item
Sort candidate items according to the prediction probability and recommend them

45. What is Collaborative Filtering?
Traced back to the Information Tapestry project at Xerox PARC
users annotate documents that they have read and the system recommends them new documents to read
Expanded to “automatic” CF in the works of Resnick, Riedl, Maes
the process of filtering items using the opinions of other people like you
Key idea: people who agreed with me in the past, will also agree in the future
Compare to Content-based recommendation:
items with features similar to those I liked before will be also liked by me

46. User-based CF Item 1 Item 2 Item 3 Item 4 Item 5 Alice 5 3 4 ??? 16/4
The input for the CF prediction algorithms is a matrix of users’ ratings on items, referred as the ratings matrix.
Item 1
Item 2
Item 3
Item 4
Item 5
Average
Alice 5 3 4
???
16/4
User1 1 2
9/4
User2
14/4
User3
12/4
User4
13/4
Target User

47. User-based CF

48. User-Based NN Recommendation
1.Select like-minded peer group for a target user
2. Choose candidate items which are not in the list of the target user but in the list of peer group
3.Score the items by producing a weighted score and predict the ratings for the given items
4.Select the best candidate items and recommend them to a target user
Redo all the procedures through 1 ~ 4 on a timely basis

49. User-based NN: User Similarity
Pearson’s Correlation Coefficient for User a and User b for all Products P rated by both users:
Pearson correlation takes values from +1 (Perfectly positive correlation) to (Perfectly negative correlation)
Average rating of user b

50. User-based NN: Rating Prediction
All users with high enough sim(a,b) form the likeminded neighborhood of the user a:
b ∈ neighbors(n)
To compute the predictions for the user a to like a candidate product p ∉ P
The items with highest prediction scores get recommended

51. Post-quiz What does it mean - “lost in hyperspace”?
What are the three stages of Adaptive Search?
What are the two main categories of Adaptive Hypermedia technologies?
Why Amazon wants you to rate products?