Self-Organized Web Usage Regularities

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Self-Organized Web Usage Regularities
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Problems of foraging information on WWW

• Slow accession
• Difficulty in finding useful information is
  related to balkanization of Web structure
• Difficulty to solve this fragmentation problem by
  designing an effective classification scheme.
• Solution
• To seek Web regularities in user behavior.

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Issues

• How to characterize the strong regularities in
  Web surfing in terms of user navigation
  strategies.
• How to present an information foraging
  agent-based approach to describing user behavior.
• Issues on web self-organization.

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Related research works

• Web mining for pattern-oriented adaptation to
  identify the inter-relationships among different
  websites, either based on the analysis of the
  contents in Web pages or based on the discovery
  of the access patterns from Web log files.
• Web data mining computing association rules,
  detecting sequential patterns, and discovering
  classification rules and data clusters.
• Web usage mining analysis of Web usage patterns,
  such as user access statistical properties,
  association rules and sequential patterns in user
  sessions, user classification and web page
  clusters based on user behavior.

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Information Foraging Agent Model (IFAM)

• Objectives to find the inter-relationship
  between the statistical observations on Web
  navigation regularities and foraging behavior
  patterns of individual agents.

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IFAM Artificial Web Space

• Artificial Web Space a collection of websites
  connected by hyperlinks.
• D(ci,cj)(S Mk1(cwik cwjk)2)1/2
• Where D(ci,cj) denotes the Euclidean distance
  between the content vectors of nodes i and j.

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IFAM Artificial Web Space

• Content Distribution Models
• T Xc, if ij
• cwni
• Xc, otherwise
• fxc normal (0,??)
• T normal(?t, ?t)
• Where fxc probability distribution of weight xc
• normal (0,??) normal distribution with mean 0
  
• and variance ??.
• T content (increment) offset on
  a topic
• ?t mean of normally distributed
  offset T
• ?t variance of normally distributed
  offset T

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IFAM Artificial Web Space

• Power-law distribution
• T Xc, if ij
• cwni
• Xc, otherwise
• fxc ??(Xc 1) (??1) , Xc gt 0, ?? gt 0
• Where fxc probability distribution of weight Xc
• ?? shape parameter of a
  power-law distribution
• T content (increment) offset on a
  topic

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IFAM Artificial Web Space

• Constructing an Artificial Web
• 1. For each topic i
• 2. Create node content vectors
• End
• 3. For each node i
• 4. Initialize the link list of node i
• 5. For each node j
• 6. If D(ci,cj) lt r
• 7. Add node j to the link list of node i
• 8. Add D(ci,cj) to the link list of node i
• end
• end
• end

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IFAM Foraging Agents

• Interest Profiles
• pm pwm1, pwm2pwmipwmM
• pwmi
• Pmi
• ?m j1pwmi
• Hm - ?mj1pmi log(pmi)
• Where pm preference vector of agent m
• pwmi weight of preference on topic i
• Hm interest entropy of user m

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IFAM Foraging Agents

• Interest Distribution Models
• 1. Normal distribution
• pwmi X p
• fxp normal(0,?u)
• Where normal(0,?u) denotes the normal
  distribution with mean 0 and variance ?u
• 2. Power-law distribution
• pwmi X p
• fxp ?u(Xp1) -?u 1, Xp gt 0, ?u gt 0
• Where ?u denotes the shape parameter of a
  power-law distribution

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IFAM Foraging in an Artificial Web Space

• Random agents have no strong interests in any
  specific topics.
• Rational agents have specific interested
  topics in mind and they forage in order to locate
  the pages that contain information on those
  topics.
• Recurrent agents Recurrent agents are those who
  are familiar with the Web structure and know the
  whereabouts of interesting contents.

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IFAM Foraging in an Artificial Web Space

• Agent Preference Updating depending on how much
  information on interesting topics the agent has
  found and how much the agent has absorbed such
  information.
• Pm(?) Pm(? - 1) - ?? cn
• pwmi 0, for pwmi (?) lt 0, i 1M
• Where ? denotes an absorbing factor in 0,1
  that implies how much information is accepted by
  agents on average.
• Pm(?) and Pm(? - 1) denote an agents
  preference vector after and before accessing
  information in page n, respectively.

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IFAM Foraging in an Artificial Web Space

• Motivation Functions
• flog(?mtv) normal(?m, ?m)
• Where ?m and ?m denote the mean and variance of
  the log-normal distribution of ?mtv, respectively
• ?mtv ?me?m step
• Where ?m and ?m denote the coefficient and rate
  of an exponential function. Step denotes the
  number of pages/notes that an agent has
  continuously visited.

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IFAM Foraging in an Artificial Web Space

• Rewarding Function
• ?Rt ?M i1(pwmi(? -1) - pwmi(?)

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IFAM Foraging in an Artificial Web Space

• Foraging
• 1. Initialize the nodes and links in an
  artificial Web space
• 2. Initialize information foraging agents and
  their interest profiles
• 3. For each agent m
• 4. While the support for the agent S lt
  max_supportm and S gt min_supportm
• 5. Find the hyperlinks inside node n that the
  agent is presently in
• 6. Select, based on pk, the hyperlink that
  connects to the next-level page
• 7. Forage to the selected page
• 8. Update the preference weights in the agents
  interest profile
• 9. Update the support function of the agent
• End
• 10. If the support for the agent S gt
  max_supportm
• 11. Agent m is satisfied with the contents and
  leaves the Web space
• Else
• 12. Agent m is dissatisfied and leaves the Web
  space
• End
• End

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Result

• The experiment shows that by applying a weighted
  linear-regression method, the higher the
  occurrence rate of a depth or a
  link-click-frequency is, the higher the weight
  will be.

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Self-Organized Agent

• To support adaptive organizations between agent,
  adding modeling technique allows agents to model
  their interactions with the environment and to
  recognize and manipulate new environmental
  scenarios to achieve organizational goals.