Access patterns organize data
Technology Research News
After more than a decade of rapid growth,
the World Wide Web has grown to more than 40 million servers. But despite
the efforts of many researchers, this abundance of information remains
Researchers from Old Dominion University are aiming to organize
large bodies of information using a method that automatically generates
links among digital objects based on the way the human brain organizes
information. The human brain contains about 100 billion neurons. "Our
methodology mimics how certain parts of the brain learn to make connections
between related information," said Aravind Elango, a researcher at Old
The method could eventually allow information repositories like
the Internet to self-organize based on the way users access information.
Self-organizing digital collections have the potential to cut search time,
make searching digital collections more intuitive, and preserve information
about the relationships among data, said Elango. "Digital information
is being lost now because information is passive. We want to put the information
itself in the driver's seat where possible, and give it the capabilities
to adapt and maintain its own integrity," he said.
The researchers tested the method using 150 data objects, or buckets
that provide information about a music band. Each bucket contains information
and also the means to manage it, including methods of interacting with
users and maintaining links to other buckets. Buckets are akin to folders
of information, but also contain the ability to display information and
the intelligence to maintain links to other folders, said Elango.
The researchers set up the buckets using random links, and as
users traversed the system links were changed according to Hebb's Law
of Learning, which is a rough model of how neurons in the human brain
work. Hebb's law postulates that the connection between a pair of neurons
becomes stronger when neurons are activated in quick succession. This
method is useful for situations in which it is not possible to train the
system using a set of correct or incorrect responses in advance, said
Each bucket keeps track of the two most recently visited buckets
and adds new links to those buckets based on users' travels, said Elango.
Over time the more relevant links come to occupy higher positions in each
In this way the system taps the judgment of users traversing the
system to rank the relevance of a link to a bucket, said Elango. "As users
traverse the system, it learns about user preferences from experience
and absorbs the users' knowledge on a subject."
The researchers tested their system on 15 users who collectively
took about 1,000 steps around the buckets, according to Elango. "The system
showed that after significant user traversal, each bucket contains a set
of links [that] are more relevant to the music band," he said. The organization
happened even though the people using the system were diverse and not
fans of one particular type of music, Elango added.
For example, the bucket containing information about the band
The Clash initially contained three random links -- to the Beatles, Glyn
Jones and Beck. After the user activity, Smashing Pumpkins was at the
top of the Clash bucket's linked list, followed by Beck, Fishbone, Nick
Lowe, The Beatles, The Smith's, The Replacements, Glyn Jones, N. W. A.,
The bucket containing information about The Smith's initially
contained random links to Elvis Costello, Tool and The Replacements. After
the user activity, The Replacements were at the top followed by Elvis
Costello, Pretty Things, Fishbone, Nick Lowe, The Beatles, The Clash,
Johnny Thunders, Kiss, and Tool.
The demonstration showed that, with sufficient intelligence, digital
objects can generate dynamic recommendations for users, said Elango. The
network reflects true references rather than accepted notions of how these
bands should relate, he added.
Because buckets can contain any information, the system can be
used with Web pages, said Elango. The method could be used in Internet
recommender systems in a variety of ways, he said. "Examples include making
the document collection of a digital archive more easily traversable,
or generating recommendations for each individual user."
The method could also be used to study the Internet's characteristics
as users would like to see it, said Elango. This is akin to allowing a
highway system to adapt to actual traffic patterns rather than laying
out roads ahead of time, he said. "The highway system would not only be
perfectly adjusted to how people travel, but also be a representation
of their travel preferences that we can learn from."
Elango's research colleagues were Johan Bollen and Michael L.
Nelson. The research was funded by Old Dominion University.
TRN Categories: Internet; Databases and Information Retrieval;
Computers and Society
Story Type: News
Related Elements: Technical paper, ""Dynamic Linking of
Smart Digital Objects Based on User Navigation Patterns" posted on CoRR
June 2/9, 2004
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