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Predicting Download Directories for Web Resources

George Valkanas Dimitrios Gunopulos

4th International Conference on Web Intelligence, Mining and SemanticsJune 3, 2014

Dept. of Informatics & TelecommunicationsUniversity of Athens, Greece

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Online User Activities

Activity ABS Survey

StatCan Survey

Infoplease Survey

Emailing 91% 93% 92%

General Web Browsing

87% > 70% 83%

Online Purchases

45% > 50% 62%

Download Content

37% ~30% 42%

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Facilitating Downloads

Save Link In Folder

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Facilitating Downloads

Save Link In Folder

Problems:• Predefined Directories• Blunt approach / No learning • UI Clutter• Tedious user management

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A principled solution

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A principled solution

Associate the navigation through the hierarchy with a cost function

One possible c.f.: Hierarchical Navigation Cost (HNC), i.e., #clicks

HNC(imgs/, docs/) = 2

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Problem Definition

Given The hierarchical structure A target directory T, where the

resource will be saved

Goal Suggest a directory S that minimizes the cost function

cf( S, T )

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Problem Definition

Given The hierarchical structure A target directory T, where the

resource will be saved

Goal Suggest a directory S that minimizes the cost function

cf( S, T )

•But if I know T, why not suggest T directly? (0 cost)

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Problem Definition

Given The hierarchical structure A target directory T, where the

resource will be saved

Goal Suggest a directory S that minimizes the cost function

cf( S, T )

•But if I know T, why not suggest T directly? (0 cost)

In this setting, we don’t know T until it’s too late!

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Casting to a classification framework Directories are potential class values T is the true target class S is the output of a classification process Web resource properties → classification features

Recommend S that best matches T Use directories from past saves as candidate classes

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Features & Distances

Feature DistanceTimestamp Exponential decay

Domain (current / referrer) Equality

Path, filename (current / referrer page)

Tokenize & Jaccard

Title Tokenize & Jaccard

Filename Tokenize & Jaccard

Extension Covariance Matrix

Keywords Jaccard

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Experimental Setup

Implement classifier as a FF plugin DiDoCtor approach Javascript 1-NN classifier

6 participants 4-month minimum use period

Baseline Last-by-domain (LBD), current browser approach Simulated, based on submitted result

Metrics Click Distance: HNC, Breadcrumbs Classification Accuracy

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Preliminary Result Analysis

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Preliminary Result Analysis

Take Home Messages1. Users have different saving pattern behavior(s)

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Preliminary Result Analysis

Take Home Messages1. Users have different saving pattern behavior(s)

2. Users have high variability in their accesses to each directory

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Click Distance - HNC

Take Home MessageSignificant reduction in number of clicks to reach target directory!

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Click Distance - HNC

Take Home MessageSignificant reduction in number of clicks to reach target directory!

Click distance gainis even higher

when consideringa breadcrumbs UI!

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Running Accuracy

Take Home MessageDiDoctor is much more accurate in predicting the download directory

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Basic Model Extensions

Feature reweightingRELIEF_F

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Basic Model Extensions

Feature reweightingRELIEF_F

Suggesting k directories

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Alternative classifiers

Take Home Messages• Classifiers can help!• DiDoCtor generally

performs the best• Accuracy is affected

by user behavior!

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Conclusions & Future work

Approach for facilitating downloads Optimization problem & classification framework

Experimentation with real users Basic model extensions

Further exploit the temporal dimension More informative features (e.g., entities) Automatic generation of directories

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Thank you!

Questions?

AcknowledgementsTo the evaluators of our pluginHeraclitus II fellowship, THALIS-GeoComp,

THALIS-DISFER, Aristeia-MMD, EU project INSIGHT