AntWeb The Adaptive Web Server Based on the Ants Behavior

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ADAPTIVE HYPERMEDIA
Presented By- Debraj Manna Raunak Pilani Gada
Kekin Dhiraj
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OUTLINE

• Introduction
• What is Hypermedia?
• Lost in Hyperspace Syndrome
• Adaptive Hypermedia
• AntWeb
• WebWatcher
• Conclusion

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HYPERMEDIA

• Hypertext
• Text, displayed on a computer, with references
  (hyperlinks) to other text that the reader can
  immediately access
• Hypermedia
• The use of text, data, graphics, audio and video
  (i.e. multimedia) as elements of an extended
  hypertext system
• All elements are linked so that the user can move
  between them at will

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CURRENT SCENARIO

• Search Engine helps in finding web pages.
• But not link within the websites.
• Lost in Hyperspace syndrome
• Too many links to choose
• But little knowledge about appropriate ones

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EXAMPLE
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EXAMPLE
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EXAMPLE
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ADAPTIVE HYPERMEDIA

• It tries to answer the lost in hyperspace
  syndrome.
• It tries to select a set of links appropriate for
  a current user. E.g.
• www.amazon.com
• Recommends books based on prior history and
  preferences of other users

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ADAPTIVE V/S ADAPTABLE HYPERMEDIA

• Primary difference between the two is the degree
  to which the adaptation process occurs
  autonomously
• Adaptive Hypermedia is a system driven
  personalization and modifications.
• Adaptable Hypermedia is user-driven.
• E.g. e-mail inbox
• Adaptable is a-priori but adaptive is
• a-posterior.

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FRAMEWORK
General Framework of Adaptive Hypermedia Systems
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AntWeb
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WHAT IS ANTWEB?

• Acts as an extended Web Server
• Treats Web Users as Artificial ants
• Doesn't modify content on page, instead just
  directs user to his/her most probable destination

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WHY ANTS?

• Drawbacks of ants
• No vision, thus no Global View
• Essentially no intelligence in single ants
• Despite this
• They are capable of finding shortest path from
  food to source
• They are adaptable to a changing environment

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HOW DO THEY DO THIS?

• Ants use chemical substance called Pheromone to
  communicate with one another
• Ants display intelligence as swarms rather than
  single units

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CHOOSING THE SHORTEST PATH
Image taken from http//blog.vettalabs.com
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USERS AS ARTIFICIAL ANTS

• AntWeb System treats users as ants and an
  information source as the goal (food)
• Server deposits Pheromone on users behalf
• Maintains large Database of all pheromone values
  at each page
• Tries to estimate what page an Ant wants to visit
  based on pheromone left by previous Ants

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BASIC APPROACH

• Pheromone value depends on quality of solution
• Heuristic value (estimate of time spent at a
  page) is also used
• Probability is calculated based on both these
  values
• AntWeb then chooses the page with the highest
  probability of being the one the Ant wants

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MATHEMATICALLY
Probability of moving from node i to node j
j
Where, ti,j is the amount of pheromone on edge
i,j a is a parameter to control the influence of
ti,j ?i,j is the desirability of edge i,j (a
priori knowledge, typically 1 / di,j) ß is a
parameter to control the influence of ?i,j
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MATHEMATICALLY(contd.)
Pheromone Depositing
Where, is the amount of
pheromone deposited on page i by ant k at
iteration p for destination d
is the tour done by ant k at iteration p
to get to destination d is the
distance of i from d in T is a parameter
that represents how the distance of i until d
in T affects decrease in pheromone deposited
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MATHEMATICALLY (contd.)
Pheromone Update
Where, ti,j is the amount of pheromone on a
given edge i,j ? is the rate of pheromone
evaporation ?ti,j is the amount of pheromone
deposited
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EXAMPLE

• Let, a visitor make the following trajectory to
  arrive to his target page 9
• 1A, 2A, 3A, 2C, 9
• Page Pheromone Deposited
• 1A 1/5
• 2A 1/4
• 3A 1/3
• 2C 1/2
• 9 1

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ADAPTING TO CHANGE IN ENVIRONMENT

• A pheromone decay coefficient is used
• So AntWeb will also consider other paths as time
  passes and choose better ones, if found
• New system also has provision for multiple
  solutions at a time thus providing more
  flexibility

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ANTWEB IN ACTION 1
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WebWatcher
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A TOUR GUIDE FOR MUSEUM

• Need for a Museum Tour Guide
• Poorly Defined Initial Interests of the visitor
• Museum contents not known to the visitor
• Help from someone who is familiar with the museum
• Steps
• Visitor describes initial interest to the guide
• Guide points out items of interest that refine
  the interests of the visitor
• Guide in turn refines its guidance through every
  such experience

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A TOUR GUIDE FOR WWW

• Acts as a Web Tour Guide
• Accompanies user from page to page
• Suggests appropriate links
• Learns from experience
• Different from keyword based search engine
• Search can not learn that machine learning
  matches neural networks

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TOUR WITH WEBWATCHER
Home Page of CMU
Image taken from http//www.cs.cmu.edu/webwatcher
/wwdemo.html
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TOUR WITH WEBWATCHER
The user can now type in an interest
Image taken from http//www.cs.cmu.edu/webwatcher
/wwdemo.html
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TOUR WITH WEBWATCHER
WebWatcher's tour begins from the same page
Image taken from http//www.cs.cmu.edu/webwatcher
/wwdemo.html
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INTERFACE
WebWatcher Interface 2
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LEARNING
Keyword accumulation at hyperlinks 2
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SUGGESTING A LINK

• Hyperlink is annotated with the interest of the
  users.
• Hyperlink description and interests are stored as
  TFIDF feature vector.
• Suggest hyperlinks by calculating similarity
  between users interest hyperlink description
• Cosine similarity is used.

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CONCLUSION

• Adaptive Hypermedia (AH) is a new but quickly
  developing area of research.
• Currently only 20 such systems are developed. 3
• Generally used in e-commerce IR hypermedia.
• It comes at the cost of efficiency.
• Experimental testing of AH system isnt as
  developed.

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REFERENCES

• 1 W. M. Teles, L. Weigang, and C. G. Ralha
  AntWeb The Adaptive Web Server Based on the
  Ants Behavior, wi, pp.558, 2003 IEEE/WIC
  International Conference on Web Intelligence
  (WI'03), 2003
• 2 T. Joachims, D. Freitag, T. Mitchell,
  WebWatcher A Tour Guide for the World Wide Web ,
  Proceedings of IJCAI97, August 1997
• 3 P. Brusilovsky, Methods and Techniques of
  Adaptive Hypermedia, User Modeling and User
  Adapted Interaction. V.6, n.2-3, pp.87-129.
  Special issue on adaptive hipertext and
  hypermedia, 1996.
• 4 M. Dorigo, V. Maniezzo, et A. Colorni, Ant
  system optimization by a colony of cooperating
  agents, IEEE Transactions on Systems, Man, and
  Cybernetics--Part B , volume 26, numéro 1, pages
  29-41, 1996

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END
Questions?
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EXTRA SLIDES
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Example to explain TF. IDF

• Document containing 100 words wherein the word
  cow appears 3 times
• TF for cow 0.03 (3 / 100)
• Now, assume 10 million documents and cow appears
  in one thousand of these
• Inverse Document Frequency (IDF) of cow ln(10
  000 000 / 1 000) 9.21
• TF-IDF score is the product of these quantities
  0.03 9.21 0.28.

Slide taken from cs626-449 s Lecture 7