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Wireless Lookout
A smarter interface for e-mail
Context: Many people and businesses rely almost
entirely on e-mail to manage diverse transactions. But
e-mail programs are optimized to manage messages, not
to-do lists. Technologies to make e-mail more useful
have tried aggregating messages that have a common
header or tagging messages as associated with specific,
predefined tasks. A new way to free workers from sifting
through copious messages—a machine-learning algorithm
that automatically keeps track of tasks, and which
e-mails are associated with them—hails from Nicholas
Kushmerick at University College Dublin and Tessa Lau at
IBM.
Methods and Results: First, the algorithm groups
e-mails according to the transactions they’re part of,
which it deduces by identifying, say, order numbers or
clients’ names. Next, messages are grouped by the events
they represent, such as shipping notifications or order
confirmations. Combining these two perspectives, the
algorithm looks for common patterns, or workflows, that
recur across a given set of transactions. For example,
e-commerce transactions typically involve order
notification, shipping notification, and messages about
delayed or modified orders.
On the basis of such patterns, the algorithm
automatically determines the status of a given
transaction. Without requiring user input or manually
labeled examples, the algorithm correctly identified the
transaction stages represented by 101 out of 111
messages in an e-commerce test set representing 39
transactions by six vendors.
Why it Matters: A 2003 survey of major industries
found that more than 90 percent of organizations use
e-mail to respond to customer inquiries, and about 70
percent use e-mail for invoicing and contract
negotiation. Kushmerick and Lau envision their algorithm
as the core of an interface that automatically organizes
e-mail by task as easily as by date or sender. By
learning workflows, the algorithm can facilitate even
specialized processes, which gives it an advantage over
techniques that rely on message headers or preformatted
content. Eventually, this technique and others like it
should help convert cluttered in-boxes into a set of
well-oiled workflows.
Source: Kushmerick,
N., and T. Lau. 2005. Automated e-mail activity
management: an unsupervised learning approach.
Proceedings of the 10th International Conference on
Intelligent User Interfaces, pp. 67–74. |