Related
Work
Personalized product-brokering agents require a profile of
the user in order to function effectively. The agent would also have to be
responsive to changes in the user's interests and be able to search and extract
relevant information from outside sources.
At MIT Media Labs, Maes and Sheth (Maes, 1994; Sheth & Maes, 1993) have come up
with a system to filter and retrieve a personalized set of USENET articles for a
particular user. This is done by creating and evolving a population of
information-filtering agents using genetic algorithms.
Some keywords will be provided by the user, and they represent the
user's interests. Weights are also assigned to each keyword, and the agents will
use them to search and retrieve articles from the relevant newsgroups. After
reading the articles, the user can give positive or negative feedback to the
agents via a simple graphical user interface (GUI). Positive feedback increases
the fitness of the appropriate agent and also the weights of the relevant
keywords (vice versa for negative feedback). In the background, the system
periodically creates new generations of agents from the fitter species, while it
eliminates the weaker ones. Initial results obtained from their experiments
showed that the agents are capable of tracking its user's interests and
recommend mostly relevant articles.
While the researchers at MIT require the user to input their
preferences into the system before a profile can be created, Crabtree and
Soltysiak (Crabtree
& Soltysiak, 1998; Soltysiak & Crabtree, 1998b) believed that the user's
profile can be generated automatically by monitoring the user's Web and e-mail
habits, thereby reducing the need for user-supplied keywords.
Their approach is to extract high information-bearing words, which
occurs frequently in the documents that are opened by the user. This is achieved
by using ProSum, [3] which
is a text summarizer that can generate a set of keywords to describe the
document and can also determine the information value of each keyword. A
clustering algorithm is then employed to help identify user's interests, and
some heuristics are used to ensure that the program could perform as much of the
classification of interest clusters as possible.
However, they have not been completely successful in their own
experiments. The researchers admitted that it would be difficult for the system
to classify all the user's interests without the user's help. Nevertheless, they
believed that their program has taken a step in the right direction by learning
user's interests with minimal human intervention.
A new product-brokering agent usually does not have sufficient
information to recommend any products to the user. Hence, it has to get product
information from somewhere else. A good source of information will be the
Internet. In order to do that, a method suggested by Pant and Menczer (2002) is to
implement a population of Web crawlers called InfoSpiders
that search the WWW on behalf of the user. Information on the Internet will be
gathered based on the user's query and then will be indexed accordingly.
These agents initially rely on traditional search engines to
obtain a starting set of URLs, which are relevant to the user's query. The
agents will then visit these Web sites and decode their contents before deciding
where to go next. The decoding process includes parsing the Web page and looking
at a small set of words around each hyperlink. A score is given based on their
relevance to the user. The link with the highest score is then selected, and the
agent visits the Web site.
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