Greasemonkey Scripts for Dating Sites

My primary complaint with all dating sites is that they make profile thumbnails too small on the Search & Match pages. This is in the sites’ best interest because it encourages users to click through to view more profiles. This gives users the sense of more activity when they look at their Visitors page, it creates more advertisement impressions, and it increases the likelihood of users messaging each other. But it’s not in the users’ best interest to click around aimlessly.

Fortunately I’ve discovered some sweet scripts from the source of my killer Facebook hacks that help correct this problem in big ways.

First you’ll need to install Greasemonkey if you use Firefox or Greasemetal if you use Chrome. If you’re using another browser I highly recommend you switch (FF ftw imo btw lol omg). Next you just install the following scripts and refresh the dating site to activate them. I’m not going to mention scripts that remove ads from specific sites because I use Adblock Plus for that.

We’ll start with my favorite dating site, OkCupid. OKC Big Pictures allows you to view the full sized original photo uploaded by a user, without the OkCupid watermark. Once installed, go to the photos tab of any user (except yourself) and click on a photo that looks like it might have a large original version. There appears to be a maximum resolution of 1500 x 1500, which is 4.3 times bigger than Facebook’s max. If you liked that trick, you’ll love this: okcupid (terrible name, I know) displays the full resolution image when you hover the mouse over any picture on the site! This is a lot more fun if you have very high bandwidth like I do (20 Mbit down at home, 80 Mbit at work). These images also appear to be stored on slower servers than the rest of OkC’s images, but it’s a very worthwhile tradeoff in my opinion. I don’t use IM on OkCupid, but if you do, try out Better OkCupid IM Windows. And finally, if you’re getting a low response rate when messaging women, plug in SuperResponse and boost it to an 80% response rate guaranteed!

OnlineWingman is a toolbar and dashboard that tracks your browsing history and response rates to help you improve your effectiveness. It doesn’t work right away, but it could be interesting to see its charts after using it for a week or two. It supports PlentyofFish and Match.com. I’m pretty disappointed that nothing else useful exists for Match and POF, the top 2 dating sites.

All you eHarmony subscribers probably wonder like I do why the hell eHarmony hates pictures so much. They show you first names as your matches, but no pictures. Am I really supposed to remember 50+ people I’ve never met by their first names? The Ultimate eHarmony Matches Table fixes this shortcoming and makes a huge improvement on the My Matches page; you’ll come to realize that eHarmony is unusable without it.

OKCupid’s MyBestFace Would Be Awesome … If It Worked

OKCupid recently unveiled MyBestFace, a feature that helps its members determine their most attractive profile pictures. You have to earn the report by voting on other members’ photos, and others do the same for yours. Each photo you post requires you to vote on 20 pairs of photos. You have to choose which person you’d rather go on a date with (and skip is not an option). According to OKCupid, this is how it works: “A group of real humans compared your photos with others’, and each time your photo was selected – or not – the information we gleaned was a complex function of how well the opposing photo did in its own report. In other words, we weren’t simply counting votes. We considered all the other votes, too, and converged rapidly on your best face.” Sounds a lot like Mark Zuckerberg’s Facemash to me.

As I voted I realized I was unfairly discriminating against certain users that were not my type. I wished I could specify demographics (at least age range) of people I voted on and people who voted on me. For example, an older woman may choose to go on a date with an older man when pitted against me, just because he’s older, which in turn reduces my score unfairly.

I was very surprised by the results of my report after running my favorite 8 pictures through it, so I decided to process more of them, and more again. Still surprised I decided to run them through a second time to determine the consistency of the results. After all, to trust the results of which photo is better than another, a photo should score higher than another in both round 1 and round 2.

I ran 44 pictures through MyBestFace twice and analyzed the numbers on a spreadsheet (update 6/22/2010 more accurate Excel version). Combining numbers from both rounds, I found that the standard deviation from one photo to another is lower than the average discrepancy between round 1 and round 2. If this is always true, that means you cannot tell which photo is better than another after only one round of comparison. Keep in mind that you have to vote on 20 photos per round per photo submitted, so submitting 44 photos twice required voting on 1,760 pairs of photos, which of course took a lot of time.

My average picture rating of both rounds was 67.36. The average difference between my picture score and 67.36 is 4.3, and the standard deviation over both rounds is 5.38. The average difference between the same photo in round 1 vs. round 2 is 5.46, which is the system’s margin of error.

Since the margin of error is greater than the standard deviation between my good and bad photos, I consider the results very inaccurate in round 1. One could argue that if I compared 2 photos in round 1 and the difference between them was greater than 5 (MyBestFace rounds to whole numbers), the higher scoring picture is indeed more attractive than the lower scoring picture. However, in the worst case scenario I had one picture jump 13 points from round 1 to round 2! And only 13.6% of my photos earned the same score in round 1 and round 2.

It would take rating another 1,760 pairs of photos to determine the reduction in the system’s margin of error after doubling the number of experiments, but I assume it would still be greater than the standard deviation from photo to photo. If that is the case, then even after 4 rounds of experimentation the system still fails to prove which photo is more desirable than another.

MyBestFace is fun to try, and it would be a very useful tool if its results were accurate, but after running this experiment I think I’m better off just asking a few friends which of my pictures are most attractive.

ChatVille: Chatroulette meets Facebook

Today when I opened my favorite instant messaging client, Digsby greeted me with an announcement: “We’re excited to announce the launch of ChatVille, a brand new Facebook game we created for discovering cool new people! ChatVille let’s [sic] you video chat with random Facebook users in a safe environment while earning compliments, unlocking badges, and leveling up in a race to become ChatVille Champion! http://bit.ly/ChatVille_Release PS: First one to unlock the Digsby Badge gets a free iPad!”

Naturally I tried it and quickly discovered a lot of bugs. Most of the time I click Next I get an error message: “Everyone is engaged in conversation. Chat with one of your Facebook Friends or click Next to try again.” That’s not fun! The only 10 people I’ve been able to connect to so far were all men. That’s also no fun, but not unexpected. I couldn’t figure out exactly how to improve my score and couldn’t find a guide anywhere.

Soon Adobe Flash Player began to crash my web browsers. All 3 of them. This problem persisted after uninstalling and reinstalling Flash Player and rebooting my PC. Now I can’t play the game at all because Flash Player crashes the browser immediately when the game loads, while Chatroulette still works just fine.

If this game worked, it could be the solution to the booming industry’s pervert infestation. Inappropriate conduct can get Facebook users banned from ChatVille quickly and permanently. But could this be enough of a difference from Chatroulette to achieve a lower male-to-female ratio than CR’s 9-to-1?

Try it yourself: http://apps.facebook.com/chatville/

Book review: What Would Google Do?

In What Would Google Do?, Jeff Jarvis conveys his lessons learned from the greatest technology success stories of the past decade. He draws on best practices from Etsy, Craigslist, Amazon, and of course Google. I took notes of interesting, new concepts as I read but sadly didn’t end up with much. It may be great for corporate old-schoolers, who Jarvis seems to be talking to, but if you’ve been following blogs and news in this space this book will feel a little slow and obvious.

I managed to solidify a few key points that I’ll take with me as I engender my next big tech company in the next year. First, the best position is to create a platform on which others can build. I can expect to earn little or no profit for a while under this model, but hooking developers on my platform is a very powerful strategy. I need to extract the minimum value from the network of developers and related web services to take the network to its maximum potential size and value. This enables my developers and partners to charge more, which increases their dependency on my platform or network. Another positive side-effect is that competitors don’t want to jump into a space where the efficient leader’s margins are low.

Today’s web 2.0 method for growth is to forgo paying for marketing and instead create something so great that users distribute it. Later revenue can be found and extracted, but we’ve seen the revenue-maximizing strategy fail on AOL and Yahoo while Google stole their users to frame the world’s most powerful advertising machine.

These are the most powerful pieces of advice I discovered in WWGD:

How can you act as a platform?

What can others build on top of it?

How can you add value?

How little value can you extract?

How big can the network atop your platform grow?

How can the platform get better learning from users?

How can you create open standards so even competitors will use and contribute to the network, and you get a share of the value?

I’ll certainly be applying some of these principals to my next ambitious venture. As far as the rest of the book, I recommend reading a summary instead, unless you’re brand new to the Web 2.0 business world.

Book review: Freakonomics – A Rogue Economist Explores the Hidden Side of Everything

In my time studying Business Management Economics at UC Santa Cruz, I came to appreciate Economics as the underlying force driving many other Social Sciences, including Politics, Sociology, Community Studies, Anthropology, and History. In Freakonomics: A Rogue Economist Explores the Hidden Side of Everything, authors Steven D. Levitt and Stephen J. Dubner assume this same premise to explore the hidden economic forces that connect seemingly unrelated phenomena in American society and history.

They do not argue that economics causes societal issues; rather they use economic models and experiments to explore complex issues, including racism, crime trends, abortion effects, medical malpractice, student and teacher cheating, and effects of parenting. They explore correlation and causality between distant patterns in society to convey an underlying human nature at work. In doing so they manage to prove that conventional wisdom is often wrong.

To me, the most interesting section of the book is what a bagel salesman’s data reveals about employee honesty in varying-sized companies and at different position ranks. Its findings “lie at the intersection of morality and economics,” and demonstrate consistent trends in theft, allowing the interpreter to actually predict theft within a company, given a few basic descriptions.

A bagel man named Feldman leaves bagels and cream cheese in office lounges and kitchens along with a wooden box and a sign requesting $1 per bagel on the honor system. By keeping perfect records (he’s formerly a financial analyst), he inadvertently invents a system to monitor rates of white collar crime.

At his own estranged office he receives a 95% payment rate because his colleagues knew him. But eventually he built his clientele up to 140 companies consuming 8400 bagels a week and the payment rate varied with distinct patters.

With enough data he learned to consider an “honest company” one that paid for 90% or more of its bagels consumed. 80-90% payment rate is annoying but tolerable, and if paid less than 80% Feldman posted a hectoring note. Even though as many as 20% of his clients steal bagels from him, his money box only got stolen 1/7,000 times.

The interesting part of his data is learning the factors shaping trends in honesty. Smaller offices tend to be more honest; a 50-employee company pays 3-5% more than a company with more than 300 employees, which can also be described as a reduction in theft as high as 60%. Unseasonably good weather reduces theft while cold weather has the opposite effect. The bad holidays include Christmas, Thanksgiving, Valentines Day and tax week, which each invoke up to a 15% increase in theft. Holidays that reduce the theft rate include Independence Day, Columbus Day and Labor Day.

Other interesting trends include the positive correlation between honesty and employees who like their boss and work. I was surprised to find an increase in theft as you move up the corporate ladder. Feldman speculates that executives cheated because of a sense of entitlement, or that perhaps cheating is what earned their place as an exec in the first place.

The conclusion of this excerpt, however, is quite positive and inspirational: The vast majority of Feldman’s customers do not steal even though no one is watching.

Freakonomics was a very fun and easy read, and not just because of my background in Econ. It’s entertaining all the way through and there are some very interesting insights into history and the nature of certain professions that you’d never know other than by reading this book. I recommend it.