final report.doc

Adaptive Web Systems Final Project JungShan Lee & Zhou Yuan

Adaptive Job Recommendation System

INFSCI 2955: Adaptive Web Systems By Peter Brusilovsky

Final Project Documentation by JungShan Lee & Zhou Yuan

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Table of Contents

1. Motivation and Acknowledgements....................................31.1 Motivation.....................................................................................................................3

1.2 Acknowledgements.......................................................................................................3

2. Requirments and Goals........................................................42.1 Requirements Analysis..................................................................................................4

2.2 Goal of Adaptive Job Recommendation........................................................................4

3. System Design and Explanation..........................................53.1 Feeds Cache Mechanism...............................................................................................6

3.2 Job Feeds Match and Ranking Method.........................................................................7

3.3 The Login Part..............................................................................................................9

3.4 Home Page..................................................................................................................10

3.5 My Keywords & Recommended Jobs.........................................................................11

3.6 Up-to-Date Search......................................................................................................14

3.7 My Saved Jobs............................................................................................................15

4. Conclusion...........................................................................17

5. Constraints and Future Work...........................................185.1 Constraints of Current System....................................................................................18

5.2 Future Work................................................................................................................18

6. References and Glossary....................................................196.1 References...................................................................................................................19

6.2 Glossary......................................................................................................................19

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1. Motivation and Acknowledgements

1.1 Motivation

Motivated by the knowledge we learned from Adaptive Web System by Peter Brusilovsky, we

designed and developed this “Adaptive Job Recommendation” system in the hope that the

user can read personalized job feeds based on keyword-level user model without losing

privacy.

1.2 Acknowledgements

The project was motivated by Peter Brusilovsky. He provided us the opportunity to develop

an Adaptive System based on the knowledge we learned from the class. We built the system

with PHP web language and MySql database. In the system, we also used some technologies

to support our development, SimplePie and Ajax with jQuery. The following are the brief

introduction of these technologies.

SimplePie is a very fast and easy-to-use class, written in PHP, which puts the “simple” back

into “really simple syndication”. Flexible enough to suit beginners and veterans alike,

SimplePie is focused on speed, ease of use, compatibility and standards compliance. You can

find more information about it at http://simplepie.org/.

All the Ajax works and editable content of this project are based on jQuery and PHP. jQuery

is a new kind of JavaScript Library. jQuery is a fast and concise JavaScript Library that

simplifies HTML document traversing, event handling, animating, and Ajax interactions for

rapid web development. jQuery is designed to change the way that you write JavaScript –

Write less, do more. The login part in this project are based on this following article

http://www.chazzuka.com/blog/?p=82 and we changed slightly.

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http://www.chazzuka.com/blog/?p=82

http://simplepie.org/


2. Requirments and Goals

2.1 Requirements Analysis

A web feed (or news feed) is a data format used for providing users with frequently updated

content. Content distributors syndicate a web feed, thereby allowing users to subscribe to it.

Making a collection of web feeds accessible in one spot is known as aggregation

(http://en.wikipedia.org/wiki/Web_feed), but with the rapid growing of web feeds it becomes

harder for single user to read personalized feeds, especially for the job hunters to read

personalized job information based on web feeds.

2.2 Goal of Adaptive Job Recommendation

The main goal of my design of “Adaptive Job Recommendation” is to enable users to get the

right job feeds information they want quickly and easily, and also save the preferred jobs for

future reference.

Besides, another goal of this system is to create an open keyword-level user model based on

which the job feeds can be filtered and personalized. Such model adds transparency and

controllability to this system. From the use’s point of view, no personal/privacy information is

required to filter feeds so that to produce personalized job feeds.

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http://en.wikipedia.org/wiki/Web_feed


3. System Design and Explanation

Here is the project tree view (Figure 1.). We will describe the important things about this

project using some file icons to make the description more user-friendly. Meantime, you can

also know the basic structure of this project.

Project Tree

Figure 1. Project Tree View

The main structure of this system shows in below organizational diagram. We wil focus on

some significant features.

Feeds Cache Mechanism

User Preferred Keywords and Weighting

Recommended Jobs by User Keywords

Up-to-Date Job Search

User Saved Jobs

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Figure 2. Organizational Diagram

3.1 Feeds Cache Mechanism

The cache folder caches the feeds fetched by SimplePie.inc .

Process:

1. You tell SimplePie what feed you want to get and where to cache it.

2. SimplePie looks to see if the feed is already cached:

1) If the cache is fresh use that.

2) If there is no cached copy at all, SimplePie will grab and cache the feed.

3) If the cache is there but it's old (SimplePie defaults to 60 minutes; configurable with

set_cache_duration()), then SimplePie will ask the feed if it has changed since the last

time we grabbed it (this is the HTTPCG part).

i. If it hasn't changed, we reset the timer on the cache's freshness and keep it for

another 60 minutes before checking again.

ii. If the cache has changed, SimplePie dumps the existing cache (since the cache

is just a copy of the data object based on the feed), and grabs a new copy of the

feed and uses it.

With the assistance of cache machnisim I don’t need to create a database table only for storing

the updated feeds. The server’s load has been reduced, too.

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3.2 Job Feeds Match and Ranking Method

The following code is the most important chunk in , which uses SimplePie fetch

feeds from target source and caculate sum weight for each feed based on user’s

keyword/weight pair. Then all the information, including feed title, permanent link,

description, posted time, and sum weight will be pushed into a 2d array $feeditem. And then

we sort this 2d array using function multisort order by ‘sum’ field descendingly. Finally,

display the feeds list in different styles.

<?php

$query1 = "SELECT * FROM jobs";

$result1 = $mysqli->query($query1);

$jobs = array();

while ($row1 = $result1->fetch_assoc()) {

$keywords = "";

$weightsum = 0;

$data = array();

$query2 = "SELECT * FROM goals WHERE uid=1 ORDER BY weight DESC";


while ($row2 = $result2->fetch_assoc()) {

if (stristr($row1['title'].$row1['description'], $row2['goal'])) {

$keywords .= "[".$row2['goal']."] ";

$weightsum = $weightsum + $row2['weight'];

}

}

$data['keywords'] = $keywords;

$data['sum'] = $weightsum;

$data['title'] = $row1['title'];

$data['link'] = $row1['link'];

$data['description'] = $row1['description'];

$data['posted_time'] = $row1['posted_time'];

array_push($jobs, $data);

}

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$query3 = "SELECT * FROM saved WHERE title='".$job['title']."' AND link='".

$job['link']."'";


echo "<div class=\"item\">";

if ($result3->num_rows > 0) {

echo "<h2><em class=\"sumweight\">[".$job['sum']."]</em> <a href=\"".

$job['link']."\">".$job['title']."</a><span class=\"saved\"><img src=\"images/saved.gif\"

title=\"You have saved this job\" /></span></h2>";

} else {

echo "<h2><em class=\"sumweight\">[".$job['sum']."]</em> <a href=\"".

$job['link']."\">".$job['title']."</a><span class=\"save_add\"><img

src=\"images/save_add.gif\" class=\"save_add\" name=\"".$job['title']."\"

id=\"".urlencode($job['link'])."\" title=\"Click to save this job\" /></span></h2>";

}

echo "<p>Matched Keywords: ".$job['keywords']."</p>";

echo "<p>".$job['description']."</p>";

echo "<p><small>Posted on ".$job['posted_time']."</small></p>";

echo "</div>";

}

?>

3.3 The Login Part

This is an Ajax user login page (Figure 3.). You can choose use MySQL database to store the

user’s login information or you can also use JSON to process the login data. Besides, you can

also define the login combination, for example, the combination of username/password,

email/password, and username/email/password.

When you put the cursor in the input field the background of field becomes highlighted at the

same time, we like it because it is user-friendly, and maybe it is useful for some user with

cognitive problems.

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Figure 3. The Login Page

3.4 Home Page

There are several important achievements in the system. We introduced each of them below,

categorized them into the suitable adaptive topics we learned from the adaptive web system

class and explained with the screenshots of the system.

Figure 4. Home Page

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3.5 My Keywords & Recommended Jobs

Once login into the system, the home page contains the primary functions in the system. The

content page was divided into two parts: “My Keywords” and “Recommended Jobs”. Below

we gave the detailed explanation of the functions.

My Keywords

On the left part of the above screenshot (Figure 4.), you can input any keywords and assign

them different weights range from 1 to 100 depending on how important you weight for the

keyword. I’m not sure whether this scalar range is the best one, but it allows the user input

many keywords and order them differently. Also, the keywords display by weight number

from greatest to smallest, in descent order. If you want to change one of your keywords, just

delete it by clicking icon and create a new one.

The adaptive topics we have covered: Open Keyword-Level User Profile

Users do not have to provide any personal information but the keywords they preferred, and

the system does not need to work on the users’ personal information as well. This feature

provides the privacy protected for job seekers (Figure 5.).

Figure 5. User Profile

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The adaptive topics we have covered: Adaptive Presentation

Users can delete the keywords by clicking on the icon directly (Figure 6.). The input

method for the weighting is colored slider, which implies the idea of vague weighting while

users do not know the exact number to insert (Figure 7.).

Figure 6. Delete Icon

Figure 7. Colored Slider

Recommended Jobs

On the left part of the above screenshot (Figure 4.), there are several Job Feed tabs, which

provide you different feeds source. Current the feed source are defined by me for testing

purpose, in the future I will let the users to add their own feeds tabs. The default feed is

“Feed1” and there is a list of recent feeds from this source. SimplePie does a great job on

fetching and parsing different feeds. Most of the feeds, no matter their formats is RSS or

Atom, they can be parsed by SimplePie. You can see the title of each feed and their brief

summary/description, the posted time as well.

The color [%] at the beginning of each feed shows you the total sum weight based on your

keywords and their corresponding weights. The feeds will display by total sum weight from

greatest to smallest. In this case, the first feed of this source might be the best feed for you

based on your defined keywords and their weight.

You can click the title of job to see all the content of it. And the icon at the end of a default

job title stands for that you can save this job to the “My Saved Jobs” if you want to view them

later, because jobs will be updated at a timely manner and it’s hard to find the old jobs that

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you might want to come back sometime. After clicking this icon it will changed into

another icon , which tells you that this is a save job.

The adaptive topics we have covered: Content-Based Recommendation

The recommended jobs we gave are filter by the keywords user inputted (Figure 8.). The jobs

are retrieved from the RSS feed we cached with SimplePie and saved in the database. The

total sum weight percentage is calculated by the sum of keywords that appeared from the title

and the description in the field of each job divided with the total sum of the user keywords.

Figure 8. Keywords Filter


The recommended jobs are displayed in a descent order of the matched weighting percentage

(Figure 9.). After each job title, user can add the job into their saved job by clicking on the

icon , and the icon changed to , which shows the saved status of the job.

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Figure 9 Ranking & Saved Icon

The adaptive topics we have covered: Usability Engineering

All the recommended jobs are separated by different sources with Tabs (Figure 10.). We

consider that this is the feature of usability, allowing more content in the same page, and we

found it matched with the paper “Usability Engineering for the Adaptive Web” recommended

from Michael Yudelson in the topic “Empirical Evaluation of Personalized Web Systems”.

Figure 10. Tabs

3.6 Up-to-Date Search

For the Recommended Jobs and My Keywords functions, we retrieved the jobs from the RSS

feed saved in the database. We considered that user might have demand to get some more

extra information from other resource. Thus, we provided this function allowing users to

search from the URLs they preferred and showing the result in our organized format,

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http://ir.exp.sis.pitt.edu/socialweb/index.php/User:Yudelson


consistent with the recommended job page (Figure 11.). Except the URLs, user can also input

keywords as search query. The result will retrieve from the URLs user inserts.

Figure 11. Search Jobs, URL is

http://www.careerbuilder.com/RTQ/JobRecommendationsRSS.aspx?

DossierDID=DNH0K06QNK5HMQS2FVM&DateStarted=2009-04-20T23%3a48%3a41

The adaptive topics we have covered: Personalized Search

The function allows user insert and search keywords in the URLs they query (Figure 11.). We

categorized this function in personalized search topic, because user can put their personal

search in our website and also get the result in a structural layout consistent with the whole

system. Also, there is an implicit expectation that user can learn and refine their

recommended jobs result by searching their own URLs. For example, users might look into

the titles of the content and figure out some important words that they can add into their own

keywords.

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3.7 My Saved Jobs

In the saved job page (Figure 12.), the jobs are saved from the recommended jobs page. With

the consistency, users can delete the saved job from the list by clicking on the delete icon .

The purpose of this function is that users can save the job they like for the future followed up.

Also, by saved jobs, users do not have to wonder how to get the job information next time.

Figure 12. Saved Jobs


Users can delete the saved job by clicking on the icon directly (Figure 12.). Meanwhile,

users can view the keywords they created to understand the condition the job was

recommended. We considered this is the adaptive feature since the same job might be

recommended to many different users; however, the ranking is depended on the weighting

each user gives. Different user gets different ranking result best matched their requirements.

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4. Conclusion

Overall, the system provides the recommended job by open keyword-level user model profile

with only keywords information from users, and content-based recommended method with

calculating the ranking from user given keyword weighting. Then, user can do the

personalized saved jobs on the recommended job page and viewing from the independent

page. The other significant feature on the system is personalized resource search, by which

user can do the keyword based search on the URLs they are interested in. In the end, we

implemented the system with a uniform layout, and took adaptive presentation into account

on the whole system with simple and consistent icons on the similar items. We hoped user can

get the overall idea of the presentation by working on this effort.

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5. Constraints and Future Work

5.1 Constraints of Current System

I believe that there is no constraint of thinking how to design a great web application, but

there are constraints of how to implement the design into real good web application.

1. The users can not add their own job feeds sources

2. There is no registeration section for creating new users, because I’m still thinking how to

make this system more open.

3. The scalar value used for keyword weight may need more resonable support.

4. The method we are using is keyword-based weight ranking, but the user sometines

doesn’t know how to decide a keyword and how to order/weight the keywords. So the

ranking results may be not very accurate.

5. The user can not read other user’s annotations.

5.2 Future Work

As future work, we are still shaping my ideas to make this system better. Maybe we should

improve the things have been mentioned above first or maybe we will change the direction of

this project later. Hard to predicate.

But we hope that we can make as many contribution as we can to enhance the process of

Semantic Web.

The following are some further improvements we might to make:

1. Evaluation – user study, experiments, questionnaires

2. Allow user to enable and disable some keywords

3. Choose a better alogrithm to improve recommendation accuracy

4. Give recommendations based on both keywords and saved jobs history

5. Insert feeds data into database at a timely manner

6. Develop some features using the theory of Collaborative filtering

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6. References and Glossary

6.1 References

[1] Bibeault, Bear. JQuery in action / Bear Bibeault, Yehuda Katz.

[2] Ayers, Danny. Beginning RSS and Atom programming / Danny Ayers, Andrew Watt.

[3] Ahn, J., Brusilovsky, P., Grady, J., He, D., and Syn, S. Y. (2007) Open user profiles for

adaptive news systems: help or harm?

[4] Gena, C., and Weibelzahl, S. (2007). Usability Engineering for the Adaptive Web. In: P.

Brusilovsky, A. Kobsa and W. Neidl (eds.): The Adaptive Web: Methods and Strategies of

Web Personalization. Lecture Notes in Computer Science, Vol. 4321, Berlin Heidelberg New

York: Springer-Verlag, pp.720-762.

6.2 Glossary

Ajax http://en.wikipedia.org/wiki/AJAX

CSS http://en.wikipedia.org/wiki/CSS

JavaScript http://en.wikipedia.org/wiki/JavaScript

jQuery http://en.wikipedia.org/wiki/Jquery

PHP http://en.wikipedia.org/wiki/PHP

SimplePie http://simplepie.org/

Web Feed http://en.wikipedia.org/wiki/Web_feed

Monster Feed http://rss.jobsearch.monster.com/rssquery.ashx?q=PHP&q=

%20Developer&q=%20XHTML&q=%20CSS&q=

%20XML&q=%20XSLT&q=%20MySQL&q=

%20Javascript&q=

%20DOM&cy=us&lid=316&re=0&pg=1&dv=1&baseurl=jobv

iew.monster.com

Indeed Feed http://rss.indeed.com/rss?q=PHP%2C+Developer

%2C+XHTML%2C+CSS%2C+XML%2C+XSLT%2C+Mysql

%2C+Sql+server%2C+Oracle%2C+javascrpt%2C+actionscript

%2C+java%2C+DOM%2C+flash&l=Pittsburgh%2C+PA

Yahoo HotJobs Feed http://hotjobs.yahoo.com/job-rss-l-Pittsburgh-PA-k-(PHP%2C

%20developer%2C%20XHTML%2C%20CSS%2C%20XML

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http://hotjobs.yahoo.com/job-rss-l-Pittsburgh-PA-k-(PHP%2C%20developer%2C%20XHTML%2C%20CSS%2C%20XML%2C%20XSLT%2C%20Mysql%2C%20sql%20server%2C%20oracle%2C%20javascript%2C%20DOM%2C%20actionscript%2C%20flash)-d-FT-d-PT-j-PERM-j-CONT-h-PHP%2C%20developer%2C%20XHTML%2C%20CSS%2C%20XML%2C%20XSLT%2C%20Mysql%2C%20sql%20server%2C%20oracle%2C%20javascript%2C%20DOM%2C%20actionscript%2C%20flash-f-kwany;_ylt=AncAXIQsqUfiHz82VgQ58xP6Q6IX


http://en.wikipedia.org/wiki/Web_feed

http://simplepie.org/

http://en.wikipedia.org/wiki/PHP

http://en.wikipedia.org/wiki/Jquery

http://en.wikipedia.org/wiki/JavaScript

http://en.wikipedia.org/wiki/CSS

http://en.wikipedia.org/wiki/AJAX


%2C%20XSLT%2C%20Mysql%2C%20sql%20server%2C

%20oracle%2C%20javascript%2C%20DOM%2C

%20actionscript%2C%20flash)-d-FT-d-PT-j-PERM-j-CONT-h-

PHP%2C%20developer%2C%20XHTML%2C%20CSS%2C

%20XML%2C%20XSLT%2C%20Mysql%2C%20sql

%20server%2C%20oracle%2C%20javascript%2C%20DOM

%2C%20actionscript%2C%20flash-f-

kwany;_ylt=AncAXIQsqUfiHz82VgQ58xP6Q6IX

Career Builder RSS Feed http://www.careerbuilder.com/RTQ/

JobRecommendationsRSS.aspx?

DossierDID=DNH0K06QNK5HMQS2FVM&DateStarted=200

9-04-20T23%3a48%3a41

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final report.doc

Documents

adaptive system

personalized job feeds

job feeds match

rapid growing of web

rapid web development

php web language

job hunters

personalized feeds