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ML_Final_Project_Report - HackMD
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<div id="doc" class="markdown-body container-fluid comment-enabled" data-hard-breaks="true"><h1 id="ML_Final_Project_Report" data-id="ML_Final_Project_Report"><a class="anchor hidden-xs" href="#ML_Final_Project_Report" title="ML_Final_Project_Report"><span class="octicon octicon-link"></span></a><span>ML_Final_Project_Report</span></h1><blockquote>
<p><span>0816001 王朋睿</span></p>
</blockquote><blockquote>
<h3 id="Smartphone-user’s-response-tendency-in-notifications" data-id="Smartphone-user’s-response-tendency-in-notifications"><a class="anchor hidden-xs" href="#Smartphone-user’s-response-tendency-in-notifications" title="Smartphone-user’s-response-tendency-in-notifications"><span class="octicon octicon-link"></span></a><strong><span>Smartphone user’s response tendency in notifications</span></strong></h3>
</blockquote><h2 id="Introduction" data-id="Introduction"><a class="anchor hidden-xs" href="#Introduction" title="Introduction"><span class="octicon octicon-link"></span></a><span>Introduction</span></h2><h3 id="Objectives" data-id="Objectives"><a class="anchor hidden-xs" href="#Objectives" title="Objectives"><span class="octicon octicon-link"></span></a><span>Objectives</span></h3><p><span>Smartphone notifications are indispensable to users, but they often demand users’ attention at inappropriate moments. As a result, whether the user will actually attend to a notification soon is very important. In order to solve this problem, I intend to attain the goals below.</span></p><ul>
<li><span>Predict whether a user is likely to attend to a smartphone notification within a certain time period</span></li>
<li><span>Knowing what kind of notification modalities will attract users</span></li>
<li><span>Construct a non-personalized model for each smartphone user</span></li>
</ul><h3 id="About-the-project" data-id="About-the-project"><a class="anchor hidden-xs" href="#About-the-project" title="About-the-project"><span class="octicon octicon-link"></span></a><span>About the project</span></h3><ul>
<li><span>Modeling and predicting user responsiveness to smartphone notification using machine learning classifiers</span></li>
<li><span>Focus on the response time from a notification</span></li>
<li><span>Refrain from using privacy-sensitive features due to privacy concerns</span></li>
<li><span>Prediction is based on physical features (sound, LED, vibration) instead of content (title, message)</span></li>
<li><span>Smartphone users may have different preferences for notification display, and a non-personalized model can be applied to each user</span></li>
</ul><h2 id="Data-Collection" data-id="Data-Collection"><a class="anchor hidden-xs" href="#Data-Collection" title="Data-Collection"><span class="octicon octicon-link"></span></a><span>Data Collection</span></h2><h3 id="Dataset-description" data-id="Dataset-description"><a class="anchor hidden-xs" href="#Dataset-description" title="Dataset-description"><span class="octicon octicon-link"></span></a><span>Dataset description</span></h3><ul>
<li><span>This dataset is from GitHub</span></li>
<li><span>Contain details of 176,195 logged notifications received on the Android devices of 14 users, in the period between 2018-05-07 and 2018-06-05</span></li>
<li><span>Include both notification features and device features</span>
<ul>
<li><span>notification features: time posted, time removed, sound, LED, vibration, etc.</span></li>
<li><span>device features: ringer mode, interactive, screen state, etc.</span></li>
</ul>
</li>
<li><span>Data comes from user’s behavior with notifications from any app and the Android OS itself</span></li>
</ul><h3 id="Data-collection-method" data-id="Data-collection-method"><a class="anchor hidden-xs" href="#Data-collection-method" title="Data-collection-method"><span class="octicon octicon-link"></span></a><span>Data collection method</span></h3><ul>
<li><span>Android OS allows application programmers to design specific notification modalities for the apps, so we can get this information from Android API</span></li>
<li><span>Get the log data of notifications from Android API calls </span><code>NotificationListenerService</code></li>
<li><span>Use this API to build a simple logging application that can trace all notification activities in the background</span></li>
</ul><h2 id="Preprocessing" data-id="Preprocessing"><a class="anchor hidden-xs" href="#Preprocessing" title="Preprocessing"><span class="octicon octicon-link"></span></a><span>Preprocessing</span></h2><h3 id="Data-clean-ups" data-id="Data-clean-ups"><a class="anchor hidden-xs" href="#Data-clean-ups" title="Data-clean-ups"><span class="octicon octicon-link"></span></a><span>Data clean-ups</span></h3><ol>
<li><span>Since the dataset is a sql file, I manually remove SQL syntax in it and then convert it to txt file</span></li>
<li><span>Drop some columns that are useless for the training model, such as id, notification_id, flag</span></li>
<li><span>Filter out any notification that its </span><code>timeremoved</code><span> equals to 0</span></li>
<li><span>Derive a feature </span><code>response_time</code><span> as the label by subtracting the dismissal time(</span><code>timeremoved</code><span>) of the notification from the issuing time(</span><code>timeposted</code><span>) of the notification</span></li>
<li><span>On some devices the OS would generate many notifications which it issued and dismissed at the same time, so any notifications that had a </span><code>response_time</code><span> equal to 0 were also filtered out</span></li>
<li><span>Drop the features </span><code>timeposted</code><span> and </span><code>timeremoved</code></li>
</ol><h3 id="Feature-transform" data-id="Feature-transform"><a class="anchor hidden-xs" href="#Feature-transform" title="Feature-transform"><span class="octicon octicon-link"></span></a><span>Feature transform</span></h3><p><span>Turn the problem of modeling user attentiveness into a class-prediction problem.</span></p><ol>
<li><span>Compute the mean of </span><code>response_time</code><span> as a threshold (</span><span class="mathjax raw">\(575s\)</span><span>) for classifying the user as having “high” or “low” attentiveness to the notification they receive</span></li>
<li><span>Label each notification by the threshold</span>
<ul>
<li><span>if response_time < threshold, then </span><code>y=1</code><span> for high attentiveness</span></li>
<li><span>if response_time >= threshold, then </span><code>y=0</code><span> for low attentiveness</span></li>
</ul>
</li>
<li><span>Drop the feature </span><code>response_time</code></li>
</ol><p><span>If the model predicts that a notification is high attentiveness(y=1), then we consider the user will tap into this notification, and vice versa.</span></p><h3 id="Transform-data-format" data-id="Transform-data-format"><a class="anchor hidden-xs" href="#Transform-data-format" title="Transform-data-format"><span class="octicon octicon-link"></span></a><span>Transform data format</span></h3><ol>
<li><span>Divide the dataset into </span><code>y(attentiveness)</code><span> and </span><code>X</code></li>
<li><span>Encode </span><code>X</code><span> by </span><strong><span>OrdinalEncoder()</span></strong><span> because the feature </span><code>application_name</code><span> is string type</span></li>
<li><span>Suuffle the data</span></li>
</ol><h2 id="Data-Analysis" data-id="Data-Analysis"><a class="anchor hidden-xs" href="#Data-Analysis" title="Data-Analysis"><span class="octicon octicon-link"></span></a><span>Data Analysis</span></h2><p><img src="https://i.imgur.com/wmrxPfU.png" alt="" loading="lazy"></p><p><img src="https://i.imgur.com/saQVlT5.png" alt="" loading="lazy"></p><p><img src="https://i.imgur.com/PaPIK17.png" alt="" loading="lazy"></p><p><img src="https://i.imgur.com/McrO2sj.png" alt="" loading="lazy"></p><h4 id="Value-frequency-of-every-feature" data-id="Value-frequency-of-every-feature"><a class="anchor hidden-xs" href="#Value-frequency-of-every-feature" title="Value-frequency-of-every-feature"><span class="octicon octicon-link"></span></a><span>Value frequency of every feature</span></h4><p><img src="https://i.imgur.com/Ao374g9.png" alt="" loading="lazy"></p><h2 id="Models" data-id="Models"><a class="anchor hidden-xs" href="#Models" title="Models"><span class="octicon octicon-link"></span></a><span>Models</span></h2><p><span>Using SciKit-Learn package</span></p><ul>
<li><strong><span>Naïve Bayes</span></strong><span> (with Laplace smoothing)</span></li>
<li><strong><span>Random Forest</span></strong><span> (10 trees)</span></li>
<li><strong><span>SVM</span></strong><span> (RBF kernel with gamma=0.7)</span></li>
<li><strong><span>MLPClassifier</span></strong><span> (hidden_layer_sizes=(200,))</span></li>
</ul><h2 id="Results" data-id="Results"><a class="anchor hidden-xs" href="#Results" title="Results"><span class="octicon octicon-link"></span></a><span>Results</span></h2><ul>
<li><span>For each model, a 10-fold cross-validation was performed</span></li>
<li><span>Train the model with and without </span><code>user_id</code></li>
</ul><h4 id="With-user_id" data-id="With-user_id"><a class="anchor hidden-xs" href="#With-user_id" title="With-user_id"><span class="octicon octicon-link"></span></a><span>With user_id</span></h4><p><img src="https://i.imgur.com/P04BuZ5.png" alt="" loading="lazy"></p><h4 id="Without-user_id" data-id="Without-user_id"><a class="anchor hidden-xs" href="#Without-user_id" title="Without-user_id"><span class="octicon octicon-link"></span></a><span>Without user_id</span></h4><p><img src="https://i.imgur.com/J969QSf.png" alt="" loading="lazy"></p><ul>
<li><span>All of the models have acceptable performance, and the SVM model makes the most precise prediction, it achieves approximately </span><span class="mathjax raw">\(90\%\)</span><span> accuracy.</span></li>
<li><span>There is no significant difference between using </span><code>user_id</code><span> feature and not using </span><code>user_id</code><span> feature, thus we can speculate that this feature is not a key point to affect the result. However, there are more factors that need to be considered.</span></li>
</ul><h2 id="Conclusion" data-id="Conclusion"><a class="anchor hidden-xs" href="#Conclusion" title="Conclusion"><span class="octicon octicon-link"></span></a><span>Conclusion</span></h2><ul>
<li><span>This model shows the feasibility of using notification logs to predict whether a user will engage in the notification within a certain time period with good accuracy</span></li>
<li><code>user_id</code><span> doesn’t seem to improve performance significantly, hinting that personalization of predictive models might not be necessary</span></li>
<li><span>Knowing how to adjust notification settings to attract users to tap on notifications and increase users’ willingness to enter the app</span></li>
</ul><h2 id="Application" data-id="Application"><a class="anchor hidden-xs" href="#Application" title="Application"><span class="octicon octicon-link"></span></a><span>Application</span></h2><p><span>Using this model, we can implement a service that automatically predicts and updates users’ availability to attend to a notification by viewing their notification settings. And then send the notifications at a time when they are likely to see the notifications. In this way, notifications might not cause potential disruption to their tasks.</span></p><p><span>Also, this result can be applied in many applications.</span></p><ul>
<li><span>Help developers determine the most suitable notification form according to different purposes</span></li>
<li><span>Help applications to catch user’s attention by push notifications</span></li>
<li><span>Improve the experience of smartphone users on notifications</span></li>
</ul></div>
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//toc right
var paddingRight = parseFloat(markdown.css('padding-right'));
var right = ($(window).width() - (markdown.offset().left + markdown.outerWidth() - paddingRight));
toc.css('right', right + 'px');
//affix toc left
var newbool;
var rightMargin = (markdown.parent().outerWidth() - markdown.outerWidth()) / 2;
//for ipad or wider device
if (rightMargin >= 133) {
newbool = true;
var affixLeftMargin = (tocAffix.outerWidth() - tocAffix.width()) / 2;
var left = markdown.offset().left + markdown.outerWidth() - affixLeftMargin;
tocAffix.css('left', left + 'px');
} else {
newbool = false;
}
if (newbool != enoughForAffixToc) {
enoughForAffixToc = newbool;
generateScrollspy();
}
}
$(window).resize(function () {
windowResize();
});
$(document).ready(function () {
windowResize();
generateScrollspy();
});
//remove hash
function removeHash() {
window.location.hash = '';
}
var backtotop = $('.back-to-top');
var gotobottom = $('.go-to-bottom');
backtotop.click(function (e) {
e.preventDefault();
e.stopPropagation();
if (scrollToTop)
scrollToTop();
removeHash();
});
gotobottom.click(function (e) {
e.preventDefault();
e.stopPropagation();
if (scrollToBottom)
scrollToBottom();
removeHash();
});
var toggle = $('.expand-toggle');
var tocExpand = false;
checkExpandToggle();
toggle.click(function (e) {
e.preventDefault();
e.stopPropagation();
tocExpand = !tocExpand;
checkExpandToggle();
})
function checkExpandToggle () {
var toc = $('.ui-toc-dropdown .toc');
var toggle = $('.expand-toggle');
if (!tocExpand) {
toc.removeClass('expand');
toggle.text('Expand all');
} else {
toc.addClass('expand');
toggle.text('Collapse all');
}
}
function scrollToTop() {
$('body, html').stop(true, true).animate({
scrollTop: 0
}, 100, "linear");
}
function scrollToBottom() {
$('body, html').stop(true, true).animate({
scrollTop: $(document.body)[0].scrollHeight
}, 100, "linear");
}
</script>
</body>
</html>