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\begin{figure*}[t]
\centerline{Message Filtering for Preference and Serendipity}
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%%title{Intelligent Filtering; Based on Key Words Only?}
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\begin{figure}[t]
\centering
{\em Ann Lantz} \\
Dpt of Computer and System Science \\
Stockholm University \\
Electrum 230, S-164 40 Kista, Sweden \\
+46 8 16 16 ?? \\
alz@dsv.su.se
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%%\centering
%%{\em Fredrik Kilander} \\
%%Dpt of Computer and Systems Sciences \\
%%Stockholm University \\
%%Electrum 230, S-164 40 Kista, Sweden \\
%%+46 8 16 16 02 \\
%%fk@dsv.su.se
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%%\section*{ABSTRACT}
\noindent{\large{\sf ABSTRACT}} \\
Text of abstract.

%%\section*{KEYWORDS}
\noindent{\large{\sf KEYWORDS:}}
Text classification, information filtering, computer-mediated communication.

%%\section*{INTRODUCTION}
\noindent{\large{\sf INTRODUCTION}} \\
%% Problem description, long-term goal (conference system), short-term goal
%% (usenet news). Common problem: text classification and message sequencing.

Keywords and phrases are in common use in information filtering 
(\cite{malone87,malone92,sheth93}),
but other criteria could be usable, especially in combination with
machine learning techniques. By studying how users of Usenet 
News select and rate information, we have searched for additional
filtering criteria.

%%\section*{USER STUDIES}
\noindent{\large{\sf USER STUDIES}} \\
%%
%%- STUDIES OF USERS WAY OF SELECTING 
%%INFORMATION AND THEIR NEED OF 
%%INFORMATION FILTERING
%%
In a first pilot study \cite{lantz93} the aim was to investigate how
experienced users of Usenet News selected their information and how
they reacted towards assisting the system in different ways i.\ e.\ by
rating contributions, write key words, use rules or by letting the
system take part of the users profile. Four subjects were instructed
to perform a set of tasks in Usenet News and think aloud during the
session. After the session the subjects were interviewed.

The results pointed at the fact that the users selected
their information by taking notice of the information in the message header;
mainly key words they saw in the title, and partly by browsing through
the text, where keywords and general layout are used for selection. In
an unfamiliar news group the selection took longer time, apparently
since the keywords were unfamiliar. The subjects were positive to
rating contributions, adding keywords and also to use rules.  On the
question if the system should be allowed to take part of the users
profile, one subject expressed the feeling of need for privacy
i.\ e.\ not wanting the system to know all of the groups he was entering.

In a second pilot study \cite{lantz94} the aim was to investigate the
criteria for selecting information used by experienced readers of
Usenet News. Three subjects were instructed to select ten
contributions from five newsgroups each and to think aloud while they
read. They were asked to read the contributions and place each in one
of three categories (read now, save, discard).
%%after each contribution 
%%rate it according to a three-step scale.

%%The selections were done by reading or browsing through the texts. 
The subjects used depth of comment, keywords, patterns in the
contribution, amount of cited text, questions, length of text,
alphabetical signs or numbers to form their decicions. These criteria
have been devided into two groups; pattern recognitions and demands
reading.

A questionnaire have recently been distributed in a Swedish 
Usenet News newsgroup. The aim with the 
study was to answer the following questions:
\begin{itemize}
\item do users of Usenet News experience a need for filtering?
\item which criterias do the users adopt to find or avoid
information in Usenet News?  
\item in what way are the users willing to support the system?
\item how important do the users regard the information 
presented in Usenet News?
\item do the users experience that they have enough time to 
spend with the news system?
\end{itemize}

Twenty subjects were included in the study and the 
preliminary results show that:
\begin{itemize}
\item the need for filtering were experienced by 19 of the 
subjects and they did not express a need for filtering 
information in the meetings not subscribed.
\item criteria 
\end{itemize}

%%We hope that the results can help us to focus on the most 
%%relevant parts of the system while concerning the filtering 
%%mechanisms.



%%\section*{TECHNICAL WORK}
\noindent{\large{\sf TECHNICAL WORK}} \\
In order to facilitate automatic recognition of messages that the user
is likely to regard as interesting, some kind of feedback model is
required.  The feedback model enables the user to provide the system
with information about preferred messages.  One of the most
uncomfortable ways to do this, is to force the user to formulate such
preferences in terms of a rule system with rigid and narrow semantics.
Popular readers for Usenet News commonly employs such rules.  With
them, the burden of formulation into operative and non-conflicting
rules is put on the human.

Other schemes has been proposed where the user supplies either an
explicit or implicit rank on each message. The rankings are then
treated as classification labels and machine learning techniques are
employed to distill an operative and efficient distinction. Explicit
ranking of messages, however, distracts the user from the primary task
of reading. The burden is attenuated by inductive machine learning
techniques which commonly require many correctly and consistently
ranked examples and counter-examples. Implicit ranking does not put
any extra strain on the user, but must make a number of ad hoc
assumptions with respect to the behaviour of each user. Such
assumptions include the expected reading time per lines of text, that
cited text is not read in detail, that the user is actually reading
and not answering the telephone and that the user does not reject
relevant messages by accident.

We are of the opinion that the user is willing and able to supply an
automatic classification system with a small number (five or less) of
{\em prototypical} messages for each conceptually distinct and
interesting topic. We also believe that the user is prepared to
(although not required) review the installed prototypes in order to
delete text, add important fragments or emphasize critical parts of
the existing text. 

Our approach to the problem is to view it as an information retrieval
process, where the user has a number of outstanding queries.  Each
query corresponds to a topic the user is interested in. The document 
collection at any time, is the sequence of unread messages provided
by the subscribed newsgroups. This view has earlier been presented
by Belkin and Croft \cite{belkin92}.

Our work is similar to that of Sheth \cite{sheth94}, Morita and Shinoda
\cite{morita94}, and Yan and Garcia-Molina \cite{yan93}. Like them, we
have decided to use a simple statistical similarity measure for
queries and documents. We have evaluated two such measures, the
well-known {\em cosine} measure (Salton \cite{salton83}), and {\em
sub-string indexing} (Morita and Shinoda \cite{morita94}).

However, in response to empirical studies by Lantz on readers of
Usenet News, we also want to take advantage of the structural
information contained in each message. We believe that a message
filter (in contrast to an {\em information} filter) should be able to
gain discriminative power from the physical appearence of a message.
In quantitative terms: the amount of cited text, the layout of the
text, the proportion of misspelled words or 

%%\section*{CONCLUDING REMARKS}
%%\noindent{\large{\sf CONCLUDING REMARKS}}

%% Query maintenance: clustering prototypes around a centroid. When two
%% centroids become too close, ask the user if they can be merged.
%% Similar and dissimilar terms in multiple examples: how can they
%% be used? Each pair of examples will have some common terms and
%% some non-common. How to treat terms common between queries?
%% What does user-assigned weight really mean?

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