W2E Day 3: Morning Presentations
by Tom Hughes-Croucher of Yahoo
One interesting technical note Tom highlighted: to make use of multi-core hardware with an event-driven server, new threads or processes need to be spun off by hand for heavy work (as opposed to automatically for each connection as in Apache). Although Node does support the fork system call, it also implements the HTML5 Web Workers spec. That means rather using slightly different concepts to spawn helpers on the client and the server, developers can reuse their knowledge when writing code in both places.
NPM, the Node Package Manager
Mustache, a JSON-like templating language (which Twitter currently uses in JS in the client but Ruby on the server)
Express, an MVC framework similar to the lower levels of the Rails stack
Paperboy, a static file server
As well as using it as a web server, Node has an interactive shell just like Python’s or Ruby’s. Definitely going to be picking this up for my scripting needs, even though I don’t exactly do much server development.
Tom’s slides are online at http://speakerrate.com/sh1mmer.
When Actions Speak Louder Than Tweets: Using Behavioral Data for Decision-Making on the Web
by Jaidev Shergill, CEO of Bundle.com
Now here’s how to make a product focused presentation without sounding like a shill:
- Here are the resources we have that most people don’t (a large database of consumer behaviour data, including anonymized credit card purchases from a major bank, government statistics and nebulous “third party databases”)
- Here are some studies we did for our own information, whose results we think you’d find useful (“We tracked a group of people in detail and interviewed them to find out in depth how they make decisions”)
- Here’s a neat experiment we put together using these two pieces of information – we don’t even know if we’ll release it, we just wanted to find the results (and here they are)
- Oh, and here’s our actual product
Jaidev presented two theses, the first gleaned from interviewing study participants and the second from his own experience:
1. There’s more than enough information on the Web to make decisions, but 99% of it is useless for the specific person looking at it, because – especially when looking at opinions and reviews – people need to know how people that are like them feel about an option. (Here we are talking about subjective decisions like, “Is this a good restaurant?” or decisions with a lot variables like, “Does this new device fit my exact needs?”)
2. Online user-generated content is nearly useless for finding opinions because it is not filtered right. For example, review sites tend to polarize between 5 star and 1 star reviews because only users with strong opinions bother to rate, so all reviews are distorted. Many people filter by their social circle since their friends (mentions on Facebook, Twitter, etc) have things in common so their recommendations carry more weight, but this means that recommendations are skewed towards options with the latest hype. It turns out people are much better at reporting new things they just found than what they actually use longterm.
To illustrate this, Jaidev presented an experiment in which he used his company’s credit card database to build a restaurant recommendation system, by drawing a map between restaurants based on where people spent their money, how often they returned, and how much they spent there. Type in a restaurant you like and the system would return a list of where else people who ate at that restaurant spend their money. Rather than a subjective rating, the tool returns a “loyalty index” quantifying how much repeat business the restaurant gets. Presumably this will be more useful to you than a general recommendation because the originators of this data share at least one important factor with you: a love of the original restaurant.
The result was that a restaurant which was highly recommended on both review sites and in Jaidev’s circle rated very low. Compared to restaurants with similar food and prices, customers returned to this one far less often and spent far less. Reading reviews in depth revealed that, while the highest ratings praised the food quality, middling ratings sais that the food was good but management was terrible, with very slow service and high prices. Equally good food could be found elsewhere for less price and hassle. This information was available in reviews, but hard to find since it was drowned out by the all-positive or all-negative reviews.
So the main point to take away from the presentation is: hard data through data mining is still more valuable than the buzz generated through social media. Which is obvious, but a good point to repeat at this conference which is full of people who are so excited about adding social components to everything.
Jaidev did a great job of demonstrating the value of his company’s data set without actually sounding like he was selling it. He only demonstrated bundle.com itself briefly: it seems to be a money management site which allows users to compare their financial situation to the average and median to answer questions like, “Am I spending too much on these products?” and, “How much should I budget for this?”. The example Jaidev showed was an interactive graph of the cost of pet ownership. Looks like a useful site.
Alas, the equally useful looking restaurant recommender was only a proof of concept and is not released to the public. (And only covers Manhattan.) Email firstname.lastname@example.org if you want to see it made public.
(While I’m attending this conference on behalf of Research In Motion, this blog and its contents are my personal opinions and do not represent the views of my employers. How does the unicorn breathe?)