Overall many of the talks I attended where using latent factor models and stochastic gradient descent. A few mentions of gradient boosted trees. Good stuff. My favorite talks…

• Charles Elkin’s “A Log-Linear Model with Latent Features for Dyadic Prediction”. A scalable method to do collaborative filtering using both latent features and “side information” (user/item content features). Can’t wait to try it out! Here’s a link.
• Peter Norvig’s Keynote about data mining at Google. Some tidbits:
  • Google mostly uses unsupervised or semi-supervised learning, both because of the cost of labeling and because labels themselves can be an impediment to higher accuracy.
  • He had this great graph of the accuracy of several algorithms for the word sense disambiguation task plotted against the amount of data used in training. The best performing algorithm always underperformed the worst algorithm when it was given an order of magnitude more data. A great argument for simple learning algorithms at very large scale.
  • They are very interested in transfer learning
• KDD Cup. Topic was music recommendation using Yahoo’s data.
  • Many of the same ideas and observations as in the Netflix Prize. Neighbor models and latent factor models trained with stochastic gradient descent seemed pervasive.
  • Ensembles were necessary to win, but the accuracy improvement wasn’t huge over the best individual models. Quite the argument for not using ensembles in industry.
  • Yahoo organizers made some really interesting comments about the data. Among them that the mean rating is quite different for power users, which makes sense. And the data is acquired from different UI mechanisms, if I understood correctly, which impacts the distributions.

Looking forward to tomorrow!

 ushahidi.com.  The idea here is a map based system for sharing information in a crisis.  Apparently was used following the Haiti earthquake.  Very cool!!

documentcloud.org. Idea is to facilate sharing of documents used as sources in news stories.  I could imagine this idea could grow into community or algorithmic fact checking.

1. GigaPan only accepts images that are 50 megapixels or more, which is really large.  If your image is large, but not that large, download SmillaEnlarger and increase the size a little.
2. Sign up for a GigaPan account if you don’t already have one.
3. Download GigaPan uploader.
4. Install the uploader, and upload the image.  The software was pretty easy to use.  When the upload is finished, you’ll get a url for the image that includes a 5 digit id.  One of mine was 65469, for example.
5. On the wordpress page or post where you want the image, place the following code (replacing 65469 with your image’s id):
   <iframe src=http://www.gigapan.org/media/gigapans/65469/options/nosnapshots/iframe/flash.html?height=400 frameborder=”0″ height=”400″ scrolling=”no” width=”100%”></iframe>
6. That’s it!  You’ll get something like this…

• I don’t know of any direct search method that scales to more than a dozen-sih parameters.
• Apache Commons Math has two direct search algorithms implemented in its Optimization package that are great place to start.  The package also provides a framework for defining the cost function.  Check it out: http://commons.apache.org/math/userguide/optimization.html 
• Implementations abound in which each parameter is iteratively changed, using a heuristic for direction and possibility momentum for the changes.  Evaluation of the cost function usually happens after a single parameter is updated, rather than only after an epoch.  Here is a good example lifted from a paper describing the winning solution to the Netflix Prize (http://www.netflixprize.com/assets/ProgressPrize2008_BigChaos.pdf)…

Julie Steele was wonderful to work with as an editor. And O’Reilly is kind enough to allow the chapter authors to release their own chapters in digital form. So if your interested, you can download the chapter here.

Here’s the Netflix visualization from the chapter. Click it to enlarge.

Movies in the Netflix Prize Dataset

Closeup of Netflix Prize Visualization.

Another closeup of the Netflix Prize visualization.

Fortunately, there’s a ton of great resources that are free and on the web. The very best way to get started that I can think of is to read chapter one of The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009 edition). The pdf is available online. Or buy the book on Amazon here, if you prefer.

Once you’ve read the first chapter, download R. R is an open-source statistics package/language that’s quite popular. Never heard of it? Check out this post (How Google and Facebook are using R).

Once you’ve installed R, maybe played around a little, then check out this page which describes the major machine learning packages in R. If you’re already familiar with some of the techniques, then dive in and start playing around with them in R. On the other hand, if it looks really complicated, don’t worry about it yet.

Oh, by the way, if you want to start playing around with machine learning in R, you’ll need data. Check out the UCI Machine Learning Repository. They have both real and toy datasets. The iris dataset, for example, is famous for showing up in many research publications.

I’d suggest next reading more of The Elements of Statistical Learning. Its an excellent book. Try doing some of the programming exercises using R. If you don’t like this book, there are plenty of others. Bishop’s Pattern Recognition and Machine Learning is a famous one. It can be a little difficult depending on your math background. Tom Mitchell’s Machine Learning is another that’s often used to teach the topic.

If you’re looking for perhaps a more passive experience, or want the feel of a classrom, Andrew Ng of Stanford has posted all of his lectures online. He starts by saying that he thinks machine learning is the most exciting field in all of computer science. Here here!

Another great resource is the machine learning course MIT has posted on their OpenCourseWare site. It has the lecture notes, assignments, and more.

I’ll stop here now. More later.

The Networks
Quite a bit of interesting complexity is present in these interaction networks (the data).  They are often small-world, disassociative (unlike social networks), scale-free, and exhibit modularity.  Biologists are usually either interested in looking at larger scale cell level networks, or meaningful sub-networks called pathways, which typically are in the range of 50-500 nodes.

Making life interesting, duplicate nodes representing different states are often included.  The edges are directed, and may be hyperedges when multiple nodes necessarily interact together. And, in truth, the edges are often approximations of the actual interactions in the underlying network.  These approximations come from experimental findings published in journals.  

A First Look
This image is part of Roche Applied Science’s “Biochemical Pathways” series of wall charts.  The charts are in the style of circuit diagrams, which seems to be the most common 2-D representation of metabolic pathways.  This set seems to have been particular influential.  The appeal of this ‘map’ is likely its scale.  Viewers can spend a great deal of time exploring.  In visualization there is a notion of ‘information density’, meaning the more visual attributes used to convey the data, the more information that may be present in the visualization.  This image has a very high information density. 

Layout

In general (not just systems bio), network/graph layout (choosing where to place the nodes and edges) is done with consideration for (A) the topology network and (B) the aesthetics.  The primary topology concern is to place connected node pairs near one another and unconnected pairs apart.  The primary aesthetic concerns are to ensure that nodes do not overlap, edges do not cross, and labels are readable.    

However, nodes in systems biology often also have biologically significant locations associated with them (e.g., within a cell, or within the nucleus of a cell).  The most common way of handling this location information is to treat the layout in a standard network layout manner, but constrain nodes to a compartment/level designated as the extracellular, membrane, cytoplasm, nucleus, etc.  This visualization, created with the Cerebral plugin for Cytoscape is the best example I know of of this.

Realism

Most of the network visualization tools for systems biology create very abstract images.  However, in high quality publications, such as the journal Nature, the abstract images are often hand rendered to include more realistic imagery.  Something I would like to do more of if look at actual microscope images and behavioral models to try to usefully bridge the gap.

Visual Data Mining

There are many uses of these network visualizations for biologists and others.  One is just that they can leave a more lasting impression/memory than simple lists.  A major use case, though, is visual data mining, which may take many forms.  Followers of Tufte know that contrasts are often the most valuable element of a visualization.  This image is a straightforward example.  More sophistication visual data mining might include clustering and classification of those clusters.

Zoom and Community Involvement

Because the Roche wall charts beg to be explored, it is only natural that a tool would be created for doing so.  G-Language is an open source shell that supports, among other things, pathway visualization plugins.  The Genome Projector is module for G-Language which uses the Google Maps API to allow exploration and annotation.  No doubt, as systems biology network visualization tools reach later versions, more and more will support rich interaction and, perhaps, treat the visualization as a vehicle for collaboration.

Hierarchy and Metanodes
 In the networks section above, I mentioned that the networks are often modular.  The most obvious modules are organelles.  But other modules exist, such as those defined functionality.  As the above examples show, incorporation of the modularity information into the visualization often is done in a manner that makes it even more abstract.  

FINVIZ is a suite of free financial tools that takes advantage of modern visualization ideas.  The infoviz and interaction designs are certainly worth a blog post.  Here’s a look at their efforts…

1. Sector Visualization.  This visualization is a treemap implemented using the Google Maps API.   It shows how well sectors and companies (stocks) within those sectors are doing.  The attention to detail is exceptional.  The company name stays the same size on zoom, and is dual encoded using a background image.  The gain/loss is shown using shades of green/red, and is also dual encoded using text.  On mouseover details are provided in a side panel.

2. Stock Charts.  When you create a portfolio of stocks, a number of views of that portfolio.  One is a small multiples view which allows easy comparison without overlay as one has to do with Google Finance and Yahoo Finance charts.  Again, attention to detail is wonderful.  The current price is highlighted, the trendlines are nicely colored, and the volume barchart is part of the background.

 3. Trends.  They use Sparklines for trend indicators.  Well, they may just be icons (not encoded by actual data), but I’ll delude myself nonetheless.

4. News. They aggregate the news items for all the stocks in a portfolio onto one page.  Very nicely done.  Only shows the day, month, year, when they change.  Overlays chart when mouseover of price (notice the little icon to indicate this next to the word price…attention to detail). 

5. Profiles.  Again, just very nicely done, showing all of the profiles on the same page.

6.  Relative Volume Indicator.  A second vertical axis is added.

Reason #1: People Don’t Know What Data Visualization Is

People don’t know what data visualization is.  Don’t believe me?  Read the Amazon.com reviews for the book Data Visualization by Ben Fry. They contain negative comments such as “One would expect a book with the title ‘Visualizing Data’ to be crammed with pictures”.  The issue seems be that too much of the book is devoted to data and the mapping of data properties to visual properties. 

Graphic design is different from data visualization.  Graphic designers are largely free from having to deal with actual data, and from having their product emerge from data.  Graphic design components and data visualization components are often mixed, and with great success.  But they are different.  Art is not visualization.  And visualization is not art…unless it is

The above visualization (which is, in fact, by Ben Fry) is driven by the properties of two underlying datasets.  One dataset is the DNA of a monkey.  The genes (the data) are represented as very tiny white text.  A second dataset used is human DNA. It is only depicted after the difference of the two datasets has been computed.  Then the genes that are different between the monkey and human are represented in red.  Fry obviously didn’t choose which areas of the visualization would be red, the data did.  What about the monkey pic?  Even that is a visual representation of a property of the dataset…the type of the DNA dataset shown in white text.   

Reason #2: Crappy Existing Visualizations have Polluted Perception

The visualization on the left is the interface for the search engine Kartoo.  The visualization on the right is a feature CNET used to have called The Big Picture.  Both attempt to visualize data usually shown as lists (search results, related news articles) as 2D networks.  Its a nice idea, as pairwise relationship properties can be visually represented as edges.  But these particular efforts both miss the boat.  They don’t actually increase the amount of information represented by very much vs lists, while greatly increasing the mental load placed on the user trying to extract the basic information. 

Reason #3: People are Unable to Mentally Separate the View from the Data

Here’s another Ben Fry work (I was watching a video/talk of his earlier today, which is part of the reason he is so prevalent in this post).  It shows six different visualizations of the same dataset.

Many times data relates to physical objects.  In such cases people may have trouble dealing with such data as visually represented in any other manner than that which includes those physical objects.  Or another situation is one in which data has just always been depicted in a certain way, which interferes with any new depiction. 

Reason #4: Visualization is Difficult to Create and Easy to Copy

This is somewhat irrelevant, but I have had a Yahoo mail account for about a decade.  There was a good six year stretch where it never changed.  If Gmail hadn’t come along, who knows. 

When Google released Google Finance, it marked a number of firsts…the use of AJAX for stock charts (the chart itself is actually Flash), the overlay of events on the chart, and the dual time sliders.  No doubt Google spent much time and effort designing this visualization tool.  How long did it take Yahoo Finance to copy Google Finance’s chart once Google revealed it?  Not long.  Good visualization design is hard.  It’s even harder when its object is to deconstruct very complex data.  Reverse engineering a visualization is easy.

Reason #5: People Won’t Pay for Visualization?

I’m not so sure about this one, but our company’s CTO recently commented to me that he couldn’t think of any successful standalone visualization effort other than Processing. 

Applications such as Google Maps don’t count both because its free, and, more importantly, because people wouldn’t have access to the underlying data without the visualization.  I can think of a few commercial successful standalone visualizations such as this one, but surely the list is fairly short. 

- Playgrounds for Data: Inspiration from NYTimes.com Interactives
- Infovis 2007 slides on Matthew Ericson’s blog…

Just wanted to put up my slides from ETech this past week.  The talk is pretty similar to the talk I posted a few months ago, just a bit more fleshed out.
[ppt][pptx][pdf]

Unfortunately, I only made it to the conference for the day I was speaking.  Beautiful venue.  Seemed that most the buzz related to social networking issues and climate change.  Would have liked to have heard Peter Norvig’s talk.  Maybe another year.

Here’s the slides from a 2-part lecture I’m giving on ensemble learning at Indiana University.  It includes a discussion of the Netflix Prize competition, and the use of ensemble techniques in that competition.

[PDF][PPT]

This new version uses the same layout and images (well, slightly improved) as the original, but this time we tried to highlight activity in regions of Wikipedia that are predominately math or science or technology.

So we developed a program to classify Wikipedia articles as being one of these three categories (or none), based on the categories the article was assigned to and their positions in the Wikipedia category link network.

We were not surprised to see a tight cluster of math pages, in a region, I would add, which has little ‘hot’ activity.  In fact, the only article in that region with lots of activity is the article “Earth”.  It was also not surprising that technology articles are fairly spread out among the topics.

What’s striking is the science-related band (green-blue) that runs diagonal through the middle of the topic map.  I won’t share my interpretation, but rather let those interested come up with there own.  Hope you enjoy, please leave comments!

(CLICK IMAGE TO ENLARGE)

(CLICK IMAGE TO ENLARGE)

Above: The most actively edited science-related articles.

Left: Not much science here…a good indication the algorithms are working pretty well!

PDF version
PPT (Office 2007) version

Day 1

Perceptions of CBR by David Aha.  Argued that CBR may become irrelevant if there are not more theoretical results published.  Showed stats that more recent CBR publications are system-oriented to back up his argument (but may be true of AI in general).  Suggested that CBR researchers have theory envy towards machine learning practitioners. 
Note: A thought occurring to me is that CBR is more a set of design patterns, ones which are fairly accessible by the general public given the diverse interests of the delegates present.   
Credible Case Based Reasoning by Eyke Hullermeier. A formal treatment of the retrieval component of CBR (nicely timed to correspond to Aha’s argument).

Day 2

Databases and CBR by Larry Kerschberg.  History of database research.  Currently concerned with meta data, reasoning, and data providence, and moving beyond ’just a schema’ (what can happen to data item).  Suggested using CBR for web resource discovery (Wikipedia, Amazon, Facebook as parts of casebase) as major direction for CBR. Invitation to submit CBR work to special issue of Journal of Intelligent Information Systems on integrating artificial intelligence and database technologies.  Scaling, cross domain issues also brought up.  Ran out of time to cover his paper ”Knowledge Sifter”. He was quite interested in CaseML as well.
Empolis by Ralph Traphoner.  An Eclipse based tool for CBR systems.  Supports a plug-in architecture, a fact alone which makes the tool promising in my mind.  Not sure whether it is commercial or what license it carries.
Industry Panel by GE, GM, and Knexus.  Discussion of what industry wants.  Examples of money making applications was a theme, not surprisingly.  Hybrid systems and Internet data were mentioned as well.  One researcher suggested that releasing data was not worth his time.  Another mentioned he has never heard of a case based recommender in the field.  One audience member raised a concern that many outside of the CBR community see CBR as just KNN and a lot of talk.  Something that strikes me about this community is that “industry” is non-tech.

Day 3

My talk at the workshop on personalizing similarity measures. 
jColibri by Juan Recio-Garcia.  CBR system using Lucene.

Day 4

Note: Google Group on context in CBR is now up.
Note: Suggestion that the difference for most people between IR and TCBR is that TCBR systems have some inference step.
CBR in RoboCup Soccer by Hans-Dieter Burkhard. Funny videos!  I’ll try to post these.
Note: David Leake’s paper on data providence won best paper.  Congrats David!

Additional Comments

Note: A “cooking” contest is being help at next year’s ECCBR where recipes are cases.  I like the idea of a contest, and the crossover to the real world (taste of cooking matters), however I wonder if the cooking domain allows for sufficient bragging rights.
Note: Major topics in CBR continue to be similarity measures, context awareness, text processing, and health care support systems.  The new topic for next year is to involve web 2.0 technologies.

The introduction (part 1) is particularly well done, as is the portion on association rule mining (parts 7 and 8).  This is the first half of the course which has already occurred…I’ll add links as new sessions are added to Google video.

Part 1: Introduction. Discussion of locations of potentially useful data (grocery checkout, apartment door card, elevator card, laptop login, traffic sensors, cell phone, google badge, etc).  Note mild obsession with consent.  Overview of predicting future vs describing patterns, and other broad areas of data mining.  Intro to R.

Part 2: Data. Reading datasets into excel and R. Observational (data mining) vs Experimental.  Qualitative vs quantitative.  Nominal vs ordinal.  And so on…

Part 3: Data cont. More Excel and R.  Sampling.

Part 4:  Plots. Histograms, ECDF.

Part 5:  More R plots.  Overlaying multiple plots. Statistical significance.  Labels in plots.

Part 6:  More R plots.  Box plots.  Color in plots.  Installing packages.  ACCENT principles and Tufte.

Part 7: Association Rules. Measures of location. Measures of spread.  Measures of association.  Frequent itemsets.  Similar to conditional probabilities.

Part 8: More association rule mining.  Support and confidence calculations. Personalization using rules. Beyond support and confidence.Part 9: Review

Part 10: Classification.  Overview.  A negative view of decision trees.  DTs in R.  Algos for generating DTs.

Part 11: More DTs.  Gini index.  Entropy. Pruning. Precision, recall, f-measure, and ROC curve.

Part 12: Nearest Neighbor. KNN.  Support Vector Machines. Adding ‘slack’ variables, using basis functions to make the space linearly separable. Some comments on Stats vs ML. Intro to ensemble (uncorrelated) classifiers.

Part 13: Last class.  Random Forests.  AdaBoost.  Some discussion of limits of classifiers (nondeterministic observational datasets).  Clustering.  K-Means.

Before I talk anymore about the viz, though, let me mention its appearing at the NetSci 2007 Conference this week, and hopefully a varient will appear at Wikimania later this summer as well.  The viz is a huge 5 feet by 5 feet when printed, and I only include a low res, smaller version here.  At some point high quality art prints of it will appear at SciMaps for sale to fund further visualization research.

Now for the good stuff.  Much like my visualization of the netflix prize competition data, we began this piece by representing the data as a network.  In this case the nodes in the network are wikipedia articles and the edges are the links between articles.  We then (with some help from our friends at Sandia) used an algorithm to lay out all 650,000 nodes (wikipedia articles) that had at least one link in such a way that similar articles are near one another.  These are the yellow dots, which when viewed at low res give a yellow tint to the whole picture.

The sizes of the nodes (circles, dots, whatever you want to call them), are based on a model of revision activity.  So large circles indicate that an article might be controversial, or the subject of lots of vandalism, or just a topic whose content frequently changes.  We labeled only the largest nodes, to keep it readable.  There is an interactive version of this in the works based on the google maps platform which will change the labels and pictures used as the user ‘zooms’ in or out.  Stay tuned for that.

The image used for each tile was selected automatically, simply by using the first image in the most linked to article among all the articles in that tile.  We were pleasantly surprised by the quality of the images that appeared.   

Our hope for this visualization approach, which we continue to improve on, is that it could be updated in real time to give a macro sense of what is happening in Wikipedia.  I personally hope that some variation of it will end up in high schools as a teaching tool and for generating discussions.

Top 20 Most Hotly Revised Articles

• Jesus
• Adolf Hitler
• October 2003
• Nintendo revolution
• Hurricane Katrina
• India
• RuneScape
• Anarchism
• Britney Spears
• PlayStation 3
• Saddam Hussein
• Japan
• Albert Einstein
• 2004 Indian Ocean Earthquake
• New York City
• Germany
• Muhammad
• Pope Benedict XVI
• Ronald Regan
• Hinduism 

I found most interesting a cluster of movies (in blue) that I’d say are generally acclaimed. The cluster contains movies of across all genres, such as Schindler’s List, BraveHeart, and Super Size Me. Beyond that, there’s a bunch of clusters which are mostly defined by a genre such as music, sports, documentary, Imax, children’s films, or bonus material. The big blob in the center is mostly what I’d call junk movies.

I’ve labeled some movies just to give some sense of what the clusters contain. There’s an interactive version of the viz as well, so you can explore the movies for yourself…

Mouse over to get the movie titles…

http://abeautifulwww.com/wp-content/uploads/2007/04/vizview.swf

Just read an article about Google buying a small company called GapMinder which does data visualization.  I checked out the talk on the GapMinder homepage, and would recommend watching the first 10 minutes of it.  The visualization tool that is used throughout the talk is something special…easy to see Google’s interest.

Steps for installing WordPress on a GoDaddy Hosting Account

1. Get an account.  If you haven’t already, purchase a hosting account.  I chose the Deluxe plan, which really isn’t very expensive.  You’ll be emailed directions after you purchase the account.  The email will say it takes 24-48 hrs to activate, but it actually only takes 20 minutes or so.

2. Login to the “my account”.  The login is on the GoDaddy homepage.  On the my account screen, click “Hosting Account List”.  Then click “open” under control panel.  You should be at the “Hosting Manager” seen below.

3. Create a MySql Database.  WordPress stores its data on MySql.

• Click the MySql icon.  Then click “Create New Database”. Name the db “WordPress”.
• Create a db login.
• Confirm.
• Submit.  Wait a minute. Then refresh. The status should change to “setup”.
• Click the db name.
• Highlight the hostname and copy it (ctrl-c).  You’ll need it for the WordPress config file.

4.  Download WordPress.  Unzip the files.

5.  Configure the file wp-config.php.  Change the following lines using your information.

define(‘DB_NAME’, ‘wordpress’);
define(‘DB_USER’, ‘username’);
define(‘DB_PASSWORD’, ‘password’);
define(‘DB_HOST’, ‘localhost’);

6.  Upload the WordPress directory to your GoDaddy account.  You’ll need an ftp client to upload files to your account (I use Smart Ftp) and you’ll need the ftp address for your site.  Your address is ftp.yourdomain.com.  Put the files in you top level directory, that way when you go to www.yourdomain.com it will load WordPress.

7. Test WordPress.  There are detailed directions for configuring WordPress here.

Around the Web

• Here is another good tutorial.

Here’s my remoting notes dealing with amf::perl.  For context, I was working on a movie recommendation system.

Steps:

(1) On the Server side, in the cgi-bin, create a perl file, and call it, say “recommendation.pl”.   Your version of this file will look the same, except replace “/recommendation” with an appropriate subdirectory where you will put the code in step 2. 

#!perl  -w
use strict;
use AMF::Perl;

my $gateway = AMF::Perl->new;
$gateway->setBaseClassPath("./recommendation/");
$gateway->service();

(2) In a subdirectory, in this case “/recommendation”, create a perl module, in this case called “rec.pm”.  This module contains the “methodTable” which will be accessed by the flash code.  The example below has both get and set calls. 

#!perl  -w

use AMF::Perl;
use lib 'Filesystem\\Active Projects\\'; ## directory where my server side code is
use SimMan::TitleLookup; ## my own server side perl modulesuse SimMan::Recommendation;

package rec;

## must have this constructorsub new 
{
    my ($proto)=@_;
    my $self={};
    bless $self, $proto;
    return $self;
}

sub methodTable
{
    return {
        "getFeatureWeights" => {
            "description" => "Returns the current feature weights",
            "access" => "remote",
        },
        "setFeatureWeights" => {
            "description" => "Sets the current feature weights",
            "access" => "remote",
        },

        "disambiguateQuery" => {
            "description" => "Returns possible titles and their ids",
            "access" => "remote",
        },
    };
}

sub getFeatureWeights
{
    my ($self) = @_;
    my $hash = SimMan::Readers::readFeatureWeights();
    my @array;
    push @array, $hash;
    return \@array;
}

sub setFeatureWeights
{
    my ($self, $featureWeights) = @_;
    SimMan::Readers::setFeatureWeights($featureWeights);}

sub disambiguateQuery
{
    my ($self, $usertext) = @_;
    return $titles = SimMan::TitleLookup::getCandidateTitles($usertext);
}

1;

(3) On the client side, first include the necessary files and give the location of the perl files we just created.  

Then you can directly call the methods listed in the methodTable.  Notice that the results sent back by the perl module will be returned to a function called “method_result”, where method is the name of the method.

import mx.remoting.NetServices;
NetDebug.initialize();
NetServices.setDefaultGatewayUrl("http://localhost/cgi-bin/recommendation.pl");
var connection = NetServices.createGatewayConnection();
var recommender = connection.getService("rec", this);

function disambiguateQuery_Result(result){
    if (result.length == 0){
        Results.htmlText = "<b>No movies found</b>";
    }
    else{
        Results.htmlText = "<b>Are you looking for...</b><br><br>";
        for (i=0; i< result.length; i++) {
            Results.htmlText += "result[i].id + " " + result[i].title + "<br>";
        }
    }
}

searchText.enter = function(){
    recommender.disambiguateQuery(searchText.text, 10);
};

Hope this helps someone!