The Ascent of Ranking Algorithms

Algorithmic ranking is on the rise. Everywhere I turn, something or the other is being ranked analytically.

Ranking web pages based on relevance, pioneered by Google’s PageRank, maybe the best-known example of algorithmic ranking.

Also ubiquitous are ranking algorithms inside recommender systems. Given an individual’s behavior (browsing history, rating history, purchase history and so on), the idea is to rank the huge universe of things (e.g., books, movies, music) out there based on likely appeal to the individual and show the top-rankers. If you are an Amazon or Netflix customer, you have doubtless been at the receiving end of these ranked recommendations for books and movies that you may find of interest.  Plenty of complex and occasionally elegant math goes into quantifying and predicting “likely appeal” (Netflix Prize winning approach).

Despite its age, recommendation ranking is far from mature and different flavors of recommender systems are popping up every day. Just last week, BusinesWeek had a story on The Filter, a new recommendation ranking system that is allegedly leaving the other approaches in the dust (aside: One of the founders of The Filter is Peter Gabriel, legendary musician and member of Genesis, one of my favorite rock bands).

So far, I have listed “old” examples of ranking: web pages, books, movies, and music.  But  recently, I came across something new: SpotRank.

Skyhook Wireless, the company that provides location information to Apple devices (when you fire up Google Maps on your iPhone, your exact location is pinpointed using a combination of GPS information and Skyhook’s wifi database – details) announced SpotRank a few months ago.

By tracking the number of “location hits” their servers receive from Apple devices, Skyhook can determine which spots are popular and when they are popular. They capture this in the form of a popularity score and, as the name suggests, SpotRank ranks locations by their popularity score.

Next time you are in a strange part of town, have time to kill and are looking for popular spots, maybe SpotRank can help you (at least if you like hanging out with Apple fans).

Now that places are being ranked, what’s next? Ranking people?

It is already being done. Heard of UserRank?

UserRank was created by Nextjump, a NYC-based company that runs employee discount and reward programs for 90,000 corporations, organizations and affinity groups. Next Jump connects 28,000 retailers and manufacturers to the over 100 million consumers who work in the companies in its network, typically getting the merchants to offer deep discounts.

NextJump calculates a UserRank for every one of the 100m consumers in its database.

The more a user shops on our network, the higher their UserRank™ will be. Users with high UserRank™ are more likely to spend and are typically your best customers.

NextJump creates value by allowing retailers/merchants to use UserRank in offer targeting. For instance, an offer can be targeted only to consumers with a minimum UserRank.

I wonder what my UserRank is?

My final example is from the field of drug discovery. In a recent article, MIT News describes fascinating work done by researchers at MIT and Harvard on applying ranking algorithms to this area.

The drug development process typically starts with identifying a molecule that’s associated with a disease. Depending on the disease, this “target” molecule either needs to be suppressed or promoted. A drug that’s successful in treating the disease is a chemical (which, of course, is just another molecule) that suppresses or promotes the target molecule without causing bad side-effects.

How is such a drug found? Over the years, researchers have amassed a large catalog of chemicals that can help suppress or promote target molecules. From this library, drug developers find the most promising ones to use as drug candidates for further testing and clinical trials. Unfortunately,

majority of drug candidates fail — they prove to be either toxic or ineffective — in clinical trials, sometimes after hundreds of millions of dollars have been spent on them. (For every new drug that gets approved by the U.S. Food and Drug Administration, pharmaceutical companies have spent about $1 billion on research and development.) So selecting a good group of candidates at the outset is critical.

This sounds like a ranking problem: given a target molecule, rank  the chemicals in the database according to their likely effectiveness in being a viable drug for the chosen target.

The drug companies weren’t slow to recognize this, of course. They have been using machine-learning algorithms since the 90s with some success. However, the MIT-Harvard researchers showed that a

rudimentary ranking algorithm can predict drugs’ success more reliably than the algorithms currently in use.

What was the key idea?

At a general level, the new algorithm and its predecessors work in the same way. First, they’re fed data about successful and unsuccessful drug candidates. Then they try out a large variety of mathematical functions, each of which produces a numerical score for each drug candidate. Finally, they select the function whose scores most accurately predict the candidates’ actual success and failure.

The difference lies in how the algorithms measure accuracy of prediction. When older algorithms evaluate functions, they look at each score separately and ask whether it reflects the drug candidate’s success or failure. The MIT researchers’ algorithm, however, looks at scores in pairs, and asks whether the function got their order right.

(italics mine)

Rather than scoring each drug candidate in isolation and then ranking them all, the key idea was to build pairwise ranking into the construction of the matching algorithm itself.

As the data deluge gets larger and larger, finding information most relevant to one’s needs (be they mundane needs like in shopping or profound needs like in drug discovery) gets harder and harder. Perhaps this is why we are seeing ranking algorithms everywhere.

Have you seen any interesting examples of algorithmic ranking at work? Please share in the comments.

(HT to Karan Singh and Florent De Gantes for making me aware of the MIT News article and NextJump, respectively)


Selling Your Home? Exploit the Price Precision Effect!

I was leafing through a recent issue of the journal Marketing Science over the weekend and came across an article titled “The Price Precision Effect“. According to the article abstract, the authors found that, in the US residential housing market,

precise prices are judged to be smaller than round prices of similar magnitudes. For example, participants in this experiment incorrectly judged $395,425 to be smaller than $395,000.

I was intrigued! I have seen academic work on the effect of price endings, magic prices etc. in retail stores (example – behind Harvard Business Review paywall, PDF of the HBR article on Oregon State website) but I had not come across research on the psychology of price perception on big-ticket items like homes.

I delved into the details of the study that led to the finding cited above but came away disappointed.

In the study, the authors used university students in a laboratory setting, rather than actual home buyers or sellers. Further, prices were shown to the participants in such a way that each precise price-list price pair wasn’t shown to the same individual. As a result, the comparison between a precise price and its round price sibling was done indirectly across all the individuals. In short, the study setting was a bit too far from the real-world for me to take the finding seriously.

I scanned the other studies described in the article (there are five in total) and found the following in Study 5:

we collected data from actual real estate transactions and tested whether the precision or roundness of list prices influence the magnitude of the sale prices.

Actual real estate transactions. That sounded promising. What did they find?

buyers pay higher sale prices when list prices are more precise.

This is interesting and potentially useful. Just by making the list price look precise, the buyer’s willingness-to-pay goes up. How high?

consider two houses in Long Island with the same zip code and with the same number of rooms and other features; one has a list price of $485,000 and the other has a more precise list price of $484,880. Our results suggest that the house with the more precise list price will sell for about $1,200–$1,450 more.

Not huge but since the effort involved in making a price look precise is close to zero, the ROE (Return on Effort) is very high.

How exactly did the authors quantify this effect?

To assess the effect of price precision on buyer behavior, we regressed the sale price on each of our three measures of price precision.

The authors measure price precision in several different ways (e.g., number of ending zeros in the price, a 0-1 dummy variable that indicates if the price had 3 ending zeros or not) and the results are consistent across these runs.

Comfortingly, the authors controlled for a number of other variables in the regression.

We also controlled for other factors that may be correlated with both the precision of the list price and the amount of the sale price. These other factors can be broadly grouped into four categories: property-specific, agent-specific, time-specific, and market-specific.

For example, the property-specific variables included

… square footage, number of bedrooms, number of bathrooms, age of the house, as well as dummies for house style, type of heating system, etc.

The other categories were similarly represented.

Overall, I am inclined to believe this finding. While it is not an experimental study, it does use actual real-estate transactional data, carefully controls for confounding variables, and identifies an effect that doesn’t seem outlandishly large.

Best of all, it is easy to put into practice: the next time you put your home on the market, make sure the list price doesn’t have three ending zeros!