🚀 go-pugleaf

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Hello all.
	As part of a project for CS440, Artificial Intelligence, I have
implemented a system that recommends classes and professors based on the
likes and dislikes of your similarity.  If anyone has a few extra
mintues (estimate 2), please fill out the form (attached).  This will
simply add you (anonymously, if you like) to a database and recommend
what classes and professors you will most enjoy.  The more people who do
it, the better the results will be.

Thanks
-- 
Adam Carheden
adam.carheden@colostate.edu
(970) 689-1363
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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
<html>
  <head>
    <title>Auto Advisor 2000</title>
    <link rel="Stylesheet" href="aa2k.css">

  </head>

  <body>
    <center>
    <h1>Auto Advisor 2000</h1>
    <big>A Collaborative Filtering Recommender System for Classes and Professors at Colorado State University.</big>
    </center>
    <hr>

    <center>
    <form action="view.pl" method="post">
      Logon: <input name="username" type="text" size="30"><br>
      (If you don't have one, use your e-mail address or make something up!)<br>
	<input type="submit" value="Logon">
    </form>
    </center>
    <hr>

    <dl>
    <dt><strong><a name="logon?">What's my logon?</strong></dt>
    <dd>
    You logon is a word or phrase that you make up which identifies you to the
    system.  It can be anything.  It is simply a way to associate the data you
    enter with you.  If you wish to remain anonymous, make something up, such
    as "Anonymous Coward #1234".  You may, however, use your <strong>e-mail
    address</strong>.  If you do, you can later choose to have the system
    e-mail you results as more data is available.  Don't forget to write down
    your login so that you may access your data later.  Logins are case
    insensitve and can include any characters that you can manage to type in.
    All whitespace characters will be condenced to a single space.
    </dd>

    <dt><strong><a name="what?">What does it do?</a></strong></dt>
    <dd>
    Picking classes to take is pretty much a shot in the dark.  After all,
    how can you know if you're interested in a subject before you know anything
    about it?  Well, you ask your friends, of course.  Auto Advisor 2000
    takes this to the next level.  It recordes data about people's prefrences
    on both classes and professors and recommends the classes and professors 
    that you will probably enjoy.
    </dd>

    <dt><strong><a name="collaborative_filtering">What is Collaborative Filtering?</a></strong></dt>
    <dd>
    Collaborative Filtering is a class of alogrithms that attempt to filter
    data for a user based on similarity to other users.  Let's say user Tycho
    likes CS370 and CS414.  User Gabe also likes CS370.  The system might
    conclude that Tycho and Gabe like the same classes and recommend that Gabe
    take CS414.  In essence, it's a collaboration between users of the system
    to filter information.
    </dd>

    <dt><strong><a name="recommender_system">What is a Recommender System?</a></strong></dt>
    <dd>
    Recommender systems are an area of Artificial Ingelligence that attempt to
    recommend data to a user.  <a href="#collaborative_filtering">Collaborative
    Filtering</a> is one technique for recommender systems.  Recommender
    systems are used commercially to recommend products to consumser.
    </dd>
    </dl>

    <hr>
    <address><a href="mailto:adam.carheden@colostate.edu">Adam Carheden</a></address>
<!-- Created: Sun Oct 29 12:15:11 MST 2000 -->
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Last modified: Fri Nov 10 00:52:37 MST 2000
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