Can Colbert bring young Breaking Bad Fans to The Late Show?

I have to admit that I thought it was a joke at first when I heard the news that Stephen Colbert is leaving The Colbert Report and is going to host the Late Show, currently hosted by David Letterman.  The fact that he won’t be “in character” in the new show makes it more intriguing, even as it brings tremendous change to my entertainment universe.  However, while it will take some getting used to, looking at Ranker data on the two shows reveals how the change really does make sense for CBS.

Despite the ire of those who disagree with The Colbert Report’s politics, CBS is definitely addressing a need to compete better for younger viewers, who are less likely to watch TV on the major networks.  Ranker users tend to be in the 18-35 year old age bracket and The Colbert Report ranks higher than the Late Show on most every list that they both are on including the Funniest TV shows of 2012 (19 vs. 28), Best TV Shows of All-Time (186 vs. 197), and Best TV Shows of Recent Memory (37 vs. 166).  Further, people who tend to like The Colbert Report also seem to like many of the most popular shows around like Breaking Bad, Mad Men, Game of Thrones, and 30 Rock.  In contrast, correlates of the Late Show include older shows like The Sopranos and 60 Minutes.  There is some overlap as fans of both shows like The West Wing and The Daily Show, indicating that Colbert may be able to appeal to current fans as well as new audiences.

Colbert Can Expand Late Show's Audience to New Groups, yet Retain Many Current Fans.

I’ll be sad to see “Stephen Colbert” the character go.  But it looks like my loss is CBS’ gain.

- Ravi Iyer

Comments

comments

World Cup 2014 Predictions

An octopus called Paul was one of the media stars of the 2010 soccer world cup. Paul correctly predicted 11 out of 13 matches, including the final in which Spain defeated the Netherlands. The 2014 world cup is in Brazil and, in an attempt to avoid eating mussels painted with national flags, we made predictions by analyzing data from Ranker’s “Who Will Win The 2014 World Cup?” list.

Ranker lists provide two sources of information, and we used both to make our predictions. One source is the original ranking, and the re-ranks provided by other users. For the world cup list, some users were very thorough, ranking all (or nearly all) of the 32 teams who qualified for the world cup. Other users were more selective, listing just the teams they thought would finish in the top places. An interesting question for data analysis is how much weight should be given to different rankings, depending on how complete they are.

The second source of information on Ranker are the thumbs-up and thumbs-down votes other users make in response to the master list of rankings. Often ranker lists have many more votes than they have re-ranks, and so the voting data potentially are very valuable. So, another interesting question for data analysis is how the voting information should be combined with the ranking information.

A special feature of making world cup predictions is that there is very useful information provided by the structure of the competition itself. The 32 teams have been drawn in 8 brackets with 4 teams each. Within a bracket, every team plays every other team once in initial group play. The top two teams from each bracket then advance to a series of elimination games. This system places strong constraints on possible outcomes, which a good prediction should follow. For example, Although Group B contains Spain, the Netherlands, and Chile — all strong teams, currently ranked in the top 16 in the world according to FIFA rankings — only two can progress from group play and finish in the top 16 for the world cup.

We developed a model that accounts for all three of these sources of information. It uses the ranking and re-ranking data, the voting data, and the constraints coming from the brackets, to make an overall prediction. The results of this analysis are shown in the figure. The left panel shows the thumbs-up (to the right, lighter) and thumbs-down (to the left, darker) votes for each team. The middle panel summarizes the ranking data, with the area of the circles corresponding to how often each team was ranked in each position. The right hand panel shows the inferred “strength” of each team on which we based our predicted order.

Our overall prediction has host-nation Brazil winning. But the distribution of strengths shown in the model inferences panel suggests it is possible Germany, Argentina, or Spain could win. There is little to separate the remainder of the top 16, with any country from the Netherlands to Algeria capable of doing well in the finals. The impact of the drawn brackets on our predictions is clear, with a raft of strong countries — the England, USA, Uruguay, and Chile — predicted to miss the finals, because they have been drawn in difficult brackets.

- Michael Lee

Comments

comments

Lists are the Best way to get Opinion Graph Data: Comparing Ranker to State & Squerb

I was recently forwarded an article about Squerb, which shares an opinion we have long agreed with.  Specifically…

““Most sites rely on simple heuristics like thumbs-up, ‘like’ or 1-5 stars,” stated Squerb founder and CEO Chris Biscoe. He added that while those tools offer a quick overview of opinion, they don’t offer much in the way of meaningful data.

It reminds me a bit of State, another company building an opinion graph that connects more specific opinions to specific objects in the world.  They too are built upon the idea that existing sources of big data opinions, e.g. mining tweets and facebook likes, have inherent limitations.  From this Wired UK article:

Doesn’t Twitter already provide a pretty good ‘opinion network’? Alex thinks not. “The opinions out there in the world today represent a very thin slice. Most people are not motivated to express their opinion and the opinions out there for the most part are very chaotic and siloed. 98 percent of people never get heard,” he told Wired.co.uk.

I think more and more people who try to parse Facebook and Twitter data for deeper Netflix AltGenre-like opinions will realize the limitations of such data, and attempt to collect better opinion data.  In the end, I think collecting better opinion data will inevitably involve the list format that Ranker specializes in.  Lists have a few important advantages over the methods that Squerb and State are using, which include slick interfaces for tagging semantic objects with adjectives.  The advantages of lists include:

  • Lists are popular and easily digestible.  There is a reason why every article on Cracked is a list.  Lists appeal to the masses, which is precisely the audience that Alex Asseily is trying to reach on State.  To collect mass opinions, one needs a site that appeals to the masses, which is why Ranker has focused on growth as a consumer destination site, that currently collects millions of opinions.
  • Lists provide the context of other items.  It’s one thing to think that Army of Darkness is a good movie.  But how does it compare to other Zombie Movies?  Without context, it’s hard to compare people’s opinions as we all have different thresholds for different adjectives.  The presence of other items lets people consider alternatives they may not have considered in a vacuum and allows better interpretation of non-response.
  • Lists provide limits to what is being considered.  For example, consider the question of whether Tom Cruise is a good actor?  Is he one of the Best Actors of All-time?  one of the Best Action Stars?  One of the Best Actors Working Today?  Ranker data shows that people’s answers usually depend on the context (e.g. Tom Cruise gets a lot of downvotes as one of the best actors of all-time, but is indeed considered one of the best action stars.)
  • Lists are useful, especially in a mobile friendly world.

In short, collecting opinions using lists produces both more data and better data.  I welcome companies that seek to collect semantic opinion data as the opportunity is large and there are network effects such that each of our datasets is more valuable when other datasets with different biases are available for mashups.  As others realize the importance of opinion graphs, we likely will see more companies in this space and my guess is that many of these companies will evolve along the path that Ranker has taken, toward the list format.

- Ravi Iyer

Comments

comments

Ranker’s Rankings API Now in Beta

Increasingly, people are looking for specific answers to questions as opposed to webpages that happen to match the text they type into a search engine.  For example, if you search for the capital of France or the birthdate of Leonardo Da Vinci, you get a specific answer.  However, the questions that people ask are increasingly about opinions, not facts, as people are understandably more interested in what the best movie of 2013 was, as opposed to who the producer for Star Trek: Into Darkness was.

Enter Ranker’s Rankings API, which is currently now in beta, as we’d love the input of potential users’ of our API to help improve it.  Our API returns aggregated opinions about specific movies, people, tv shows, places, etc.  As an input, we can take a Wikipedia, Freebase, or Ranker ID.  The request needs to be made to http://api.ranker.com/rankings/ with “type” (e.g. FREEBASE, WIKIPEDIA, or RANKER, depending on the type of ID sent) and “id” (the specific wikipedia, freebase or Ranker ID) sent in the URL request, and our API returns JSON by default. For example, below are requests for information about Tom Cruise, using each of these IDs.

http://api.ranker.com/rankings/?id=/m/07r1h&type=FREEBASE
http://api.ranker.com/rankings/?id=2257588&type=RANKER
http://api.ranker.com/rankings/?id=31460&type=WIKIPEDIA (look for wgArticleId in the source of any wikipedia page to get a wikipedia id)

In the response to this request, you’ll get a set of Rankings for the requested object, including a set of list names (e.g. “listName”:”The Greatest 80s Teen Stars”), list urls (e.g. “listUrl”:”http://www.ranker.com/crowdranked-list/45-greatest-80_s-teen-stars” - note that the domain, www.ranker.com, is implied), item names (e.g. “itemName”:”Tom Cruise”) position of the item on this list (e.g. “position”:21), number of items on the list (e.g. “numItemsOnList”:70), the number of people who have voted on this list (e.g. “numVoters”:1149), the number of positive votes for this item (e.g. “numUpVotes”:245) vs. the number of negative votes (e.g. “numDownVotes”:169), and the Ranker list id (e.g. “listId”:584305).  Note that results are cached so they may not match the current page exactly.

Here is a snipped of the response for Tom Cruise.

[ { "itemName" : "Tom Cruise",
"listId" : 346881,
"listName" : "The Greatest Film Actors & Actresses of All Time",
"listUrl" : "http://www.ranker.com/crowdranked-list/the-greatest-film-actors-and-actresses-of-all-time",
"numDownVotes" : 306,
"numItemsOnList" : 524,
"numUpVotes" : 285,
"numVoters" : 5305,
"position" : 85
},
{ "itemName" : "Tom Cruise",
"listId" : 542455,
"listName" : "The Hottest Male Celebrities",
"listUrl" : "http://www.ranker.com/crowdranked-list/hottest-male-celebrities",
"numDownVotes" : 175,
"numItemsOnList" : 171,
"numUpVotes" : 86,
"numVoters" : 1937,
"position" : 63
},
{ "itemName" : "Tom Cruise",
"listId" : 679173,
"listName" : "The Best Actors in Film History",
"listUrl" : "http://www.ranker.com/crowdranked-list/best-actors",
"numDownVotes" : 151,
"numItemsOnList" : 272,
"numUpVotes" : 124,
"numVoters" : 1507,
"position" : 102
}

...CLIPPED....
]

What can you do with this API?  Consider this page about Tom Cruise from Google’s Knowledge Graph.  It tells you his children, his spouse(s), and his movies.  But our API will tell you that he is one of the hottest male celebrities, an annoying A-List actor, an action star, a short actor, and an 80s teen star.  His name comes up in discussions of great actors, but he tends to get more downvotes than upvotes on such lists, and even shows up on lists of “overrated” actors.

We can provide this information, not just about actors, but also about politicians, books, places, movies, tv shows, bands, athletes, colleges, brands, food, beer, and more.  We will tend to have more information about entertainment related categories, for now, but as the domains of our lists grow, so too will the breadth of opinion related information available from our API.

Our API is free and no registration is required, though we would request that you provide links and attributions to the Ranker lists that provide this data.  We likely will add some free registration at some point.  There are currently no formal rate limits, though there are obviously practical limits so please contact us if you plan to use the API heavily as we may need to make changes to accommodate such usage.  Please do let me know (ravi a t ranker) your experiences with our API and any suggestions for improvements as we are definitely looking to improve upon our beta offering.

- Ravi Iyer

Comments

comments

How Netflix’s AltGenre Movie Grammar Illustrates the Future of Search Personalization

I recently got sent this Atlantic article on how Netflix reverse engineered Hollywood by a few contacts, and it happens to mirror my long term vision for how Ranker’s data fits into the future of search personalization.  Netflix’s goal, to put “the right title in front of the right person at the right time,” is very similar to what Apple, Bing, Google, and Facebook are attempting to do with regards to personalized contextual search.  Rather than you having to type in “best kitchen gadgets for mothers”, applications like Google Now and Cue (bought by Apple) hope to eventually be able to surface this information to you in real time, knowing not only when your mother’s birthday is, but also that you tend to buy kitchen gadgets for her, and knowing what the best rated kitchen gadgets that aren’t too complex and are in your price range happen to be.  If the application was good enough, a lot of us would trust it to simply charge our credit card and send the right gift.  But obviously we are a long way from that reality.

Netflix’s altgenre movie grammar (e.g. Irreverent Werewolf Movies Of The 1960s) gives us a glimpse of the level of specificity that would be required to get us there.  Consider what you need to know to buy the right gift for your mom.  You aren’t just looking for a kitchen gadget, but one with specific attributes.  In altgenre terminology, you might be looking for “best simple, beautifully designed kitchen gadgets of 2014 that cost between $25 and $100″ or “best kitchen gadgets for vegetarian technophobes”.  Google knows that simple text matching is not going to get it the level of precision necessary to provide such answers, which is why semantic search, where the precise meaning of pages is mapped, has become a strategic priority.

However, the universe of altgenre equivalents in the non-movie world is nearly endless (e.g. Netflix has thousands of ways just to classify movies), which is where Ranker comes in, as one of the world’s largest sources for collecting explicit cross-domain altgenre-like opinions.  Semantic data from sources like wikipedia, dbpedia, and freebase can help you put together factual altgenres like “of the 60s” or “that starred Brad Pitt“, but you need opinion ratings to put together subtler data like “guilty pleasures” or “toughest movie badasses“.  Netflix’s success is proof of the power of this level of specificity in personalizing movies and consider how they produced this knowledge.  Not through running machine learning algorithms on their endless stream of user behavior data, but rather by soliciting explicit ratings along these dimensions by paying “people to watch films and tag them with all kinds of metadata” using a “36-page training document that teaches them how to rate movies on their suggestive content, goriness, romance levels, and even narrative elements like plot conclusiveness.”  Some people may think that with enough data, TripAdvisor should be able to tell you which cities are “cool”, but big data is not always better data.  Most data scientists will tell you the importance of defining the features in any recommendation task (see this article for technical detail on this), rather than assuming that a large amount of data will reveal all of the right dimensions.  The wrong level of abstraction can make prediction akin to trying to predict who will win the superbowl by knowing the precise position and status of every cell in every player on every NFL team.  Netflix’s system allows them to make predictions at the right level of abstraction.

The future of search needs a Netflix grammar that goes beyond movies.  It needs to able to understand not only which movies are dark versus gritty, but also which cities are better babymoon destinations versus party cities and which rock singers are great vocalists versus great frontmen.  Ranker lists actually have a similar grammar to Netflix movies, except that we apply this grammar beyond the movie domain.  In a subsequent post, I’ll go into more detail about this, but suffice it to say for now that I’m hopeful that our data will eventually play a similar role in the personalization of non-movie content that Netflix’s microtagging plays in film recommendations.

- Ravi Iyer

 

Comments

comments

Why Topsy/Twitter Data may never predict what matters to the rest of us

Recently Apple paid a reported $200 million for Topsy and some speculate that the reason for this purchase is to improve recommendations for products consumed using Apple devices, leveraging the data that Topsy has from Twitter.  This makes perfect sense to me, but the utility of Twitter data in predicting what people want is easy to overstate, largely because people often confuse bigger data with better data.  There are at least 2 reasons why there is a fairly hard ceiling on how much Twitter data will ever allow one to predict about what regular people want.

1.  Sampling – Twitter has a ton of data, with daily usage of around 10%.  Sample size isn’t the issue here as there is plenty of data, but rather the people who use Twitter are a very specific set of people.  Even if you correct for demographics, the psychographic of people who want to share their opinion publicly and regularly (far more people have heard of Twitter than actually use it) is way too unique to generalize to the average person, in the same way that surveys of landline users cannot be used to predict what psychographically distinct cellphone users think.

2. Domain Comprehensiveness – The opinions that people share on Twitter are biased by the medium, such that they do not represent the spectrum of things many people care about.  There are tons of opinions on entertainment, pop culture, and links that people want to promote, since they are easy to share quickly, but very little information on people’s important life goals or the qualities we admire most in a person or anything where people’s opinions are likely to be more nuanced.  Even where we have opinions in those domains, they are likely to be skewed by the 140 character limit.

Twitter (and by extension, companies that use their data like Topsy and DataSift) has a treasure trove of information, but people working on next generation recommendations and semantic search should realize that it is a small part of the overall puzzle given the above limitations.  The volume of information gives you a very precise measure of a very specific group of people’s opinions about very specific things, leaving out the vast majority of people’s opinions about the vast majority of things.  When you add in the bias introduced by analyzing 140 character natural language, there is a great deal of variance in recommendations that likely will have to be provided by other sources.

At Ranker, we have similar sampling issues, in that we collect much of our data at Ranker.com, but we are actively broadening our reach through our widget program, that now collects data on thousands of partner sites.  Our ranked list methodology certainly has bias too, which we attempt to mitigate that through combining voting and ranking data.  The key is not in the volume of data, but rather in the diversity of data, which helps mitigate the bias inherent in any particular sampling/data collection method.

Similarly, people using Twitter data would do well to consider issues of data diversity and not be blinded by large numbers of users and data points.  Certainly Twitter is bound to be a part of understanding consumer opinions, but the size of the dataset alone will not guarantee that it will be a central part.  Given these issues, either Twitter will start to diversify the ways that it collects consumer sentiment data or the best semantic search algorithms will eventually use Twitter data as but one narrowly targeted input of many.

- Ravi Iyer

Comments

comments

Hierarchical Clustering of a Ranker list of Beers

This is a guest post by Markus Pudenz.

Ranker is currently exploring ways to visualize the millions of votes collected on various topics each month.  I’ve recently begun using hierarchical cluster analysis to produce taxonomies (also known as dendograms), and applied these techniques to Ranker’s Best Beers from Around the World. A dendrogram allows one to visualize the relationships on voting patterns (scroll down to see what a dendrogram looks like). What hierarchical clustering does is break down the list into related groups based on voting patterns of the users, grouping like items with items that were voted similarly by the same users. The algorithm is agglomerative, meaning it is starts with individual items and combines them iteratively until one large cluster (all of the beers in the list)  remains.

Every beer in our dendrogram is related to another at some level, whether in the original cluster or further down the dendrogram. See the height axis on the left side? The lower the cluster is on the axis, the closer the relationship the beers will have. For example, the cluster containing Guinness and Guinness Original is the lowest in this dendrogram indicating these to beers have the closest relationship based on the voting patterns. Regarding our list, voters have the option to Vote Up or Vote Down any beer they want. Let’s start at the top of the dendrogram and work our way down.

Hierarchical Clustering of Beer Preferences

Looking at the first split of the clusters, one can observe the cluster on the right contains beers that would generally be considered well-known including Guinness, Sam Adams, Heineken and Corona. In fact, the cluster on the right includes seven of the top ten beers from the list. The fact that most of our popular beers are in this right cluster indicates that there is a strong order effect with voters more likely to select beers that are more popular when ranking their favorite beers. For example, if someone selects a beer that is in the top ten, then another beer they select is also more likely to be in the top ten. As we examine the right cluster further, the first split divides the cluster into two smaller clusters. In the left cluster, we can clearly see, unsurprisingly, that a drinker who likes Guinness is more likely to vote for another variety of Guinness. This left cluster is comprised almost entirely of Guinness varieties with the exception of Murphy’s Irish Stout. The right cluster lists a larger variety of beer makers including Sam Adams, Stella Artois and Pyramid. In addition, none of the beers in this right cluster are stouts as with the left cluster. The only brewer in this right cluster with multiple varieties is Sam Adams with Boston Lager and Octoberfest meaning drinkers in this cluster were not as brand loyal as in the left cluster. Drinkers in this cluster were more likely to select a beer variety from a different brewer. When reviewing this cluster from the first split in the dendrogram, there is clearly a defined split between those drinkers who prefer a heavier beer (stout) as opposed to those who prefer lighter beers like lagers, pilseners, pale ales or hefeweizen.

Conversely, for beers in the left cluster, drinkers are more likely to vote for other beers that are not as popular with only three of the top ten beers in this cluster. In addition, because of the larger size, the range of beer styles and brewers for this cluster is more varied as opposed to those in the right cluster. The left cluster splits into three smaller clusters before splitting further. One cluster that is clearly distinct is the second of these clusters. This cluster is comprised almost entirely of Belgian style beers with the only exception being Pliny the Elder, an IPA. La Fin du Monde is a Belgian style tripel from Quebec with the remaining brewers from Belgium. One split within this cluster is comprised entirely of beer varieties from Chimay indicating a strong relationship; voters who select Chimay are more likely to also select a different style from Chimay when ranking their favorites.  Our remaining clusters have a little more variety. Our first cluster, the smallest of the three, has a strong representation from California with varieties from Stone, Sierra Nevada and Anchor Steam taking four out of six nodes in the cluster. Stone IPA and Stone Arrogant Bastard Ale have the strongest relationship in this cluster. Our third cluster, the largest of the three, has even more variety than the first. We see a strong relationship especially with Hoegaarden and Leffe.

I was also curious as to whether the beers in the top ten were associated with larger or smaller breweries. As the following list shows,  there is an even split between the larger conglomerates like AB InBev, Diageo, Miller Coors and independent breweries like New Belgium and Sierra Nevada.

  1. Guinness (Diageo)
  2. Newcastle (Heineken)
  3. Sam Adams Boston Lager (Boston Beer Company)
  4. Stella Artois (AB InBev)
  5. Fat Tire (New Belgium Brewing Company)
  6. Sierra Nevada Pale Ale (Sierra Nevada Brewing Company)
  7. Blue Moon (Miller Coors)
  8. Stone IPA (Stone Brewing Company)
  9. Guinness Original (Diageo)
  10. Hoegaarden Witbier (AB InBev)

- Markus Pudenz

Comments

comments

Examining Regional Voting Differences with Ranker’s Polling Widget

Ranker has a new program where we offer a polling widget to partner sites who want the engagement of a poll in list format (as opposed to the standard radio button poll).  Currently, sites that use our poll (e.g. TheNextWeb or CBC) are seeing 20-50% of visitors engaging in the poll and an increase in returning visitors who want to keep track of results.  We also give partners prominent placement on Ranker.com (details of that here), but a benefit that is less obvious is the potential insights from one’s users that one can gain from the data behind a poll.  To illustrate what is possible, I’m going to use data from one of our regular widget users, Phish.net, who posted this poll on Phish’s best summer concert jams.

One piece of data that Ranker can give partners is a regional breakdown of voters.  Unsuprisingly, there were strong regional differences in voting behavior with voters from the northeast often choosing a jam from their New Jersey show, voters from the west coast often choosing a jam from their Hollywood Bowl show, voters from the south often choosing a jam from their Maryland show, voters from the midwest often choosing a jam from their Chicago show, and voters from the mountain region often choosing a jam from their show at The Gorge.  However, the interesting thing to me was that the leading jam in every region was Tweezer – Lake Tahoe from July 31st.  As someone who believes that better crowdsourced answers are produced by aggregating across bias and who has only been to 1 Phish concert, I’m definitely going to have to check out this jam.  Perhaps the answer is obvious to more experienced Phish fans, but the results of the poll are certainly instructive to the more casual music fan who wants a taste of Phish.

Below are the results of the poll in graphical format.  Notice how the shows cluster based on venue and geography except for Tweezer – Lake Tahoe which is directly in the center of the graph.

If you’re interested in running a widget poll on your site, the benefits are more clearly spelled out here and you can email us at “widget at ranker.com”.  We’d love to provide similar region based insights for your polls as well.

- Ravi Iyer

 

Comments

comments

Rankings are the Future of Mobile Search

Did you know that Ranker is one of the top 100 web destinations for mobile per Quantcast, ahead of household names like The Onion and People magazine?  We are ranked #520 in the non-mobile world.  Why do we do better with mobile users as opposed to people using a desktop computer?  I’ve made this argument for awhile, but I’m hardly an authority, so I was heartened to see Google making a similar argument.

This embrace of mobile computing impacts search behavior in a number of important ways.

First, it makes the process of refining search queries much more tiresome. …While refining queries is never a great user experience, on a mobile device (and particularly on a mobile phone) it is especially onerous.  This has provided the search engines with a compelling incentive to ensure that the right search results are delivered to users on the first go, freeing them of laborious refinements.

Second, the process of navigating to web pages (is) a royal pain on a hand-held mobile device.

This situation provides a compelling incentive for the search engines to circumvent additional web page visits altogether, and instead present answers to queries – especially straightforward informational queries – directly in the search results.  While many in the search marketing field have suggested that the search engines have increasingly introduced direct answers in the search results to rob publishers of clicks, there’s more than a trivial case to be made that this is in the best interest of mobile users.  Is it really a good thing to compel an iPhone user to browse to a web page – which may or may not be optimized for mobile – and wait for it to load in order to learn the height of the Eiffel Tower?

As a result, if you ask your mobile phone for the height of a famous building (Taipei 101 in the below case), it doesn’t direct you to a web page.  Instead it answers the question itself.

That’s great for a question that has a single answer, but an increasing number of searches are not for objective facts with a single answer, but rather for subjective opinions where a ranked list is the best result.  Consider the below chart showing the increase in searches for the term “best”.  A similar pattern can be found for most any adjective.

So if consumers are increasingly doing searches on mobile phones, requiring a concise list of potential answers to questions with more than one answer, they naturally are going to end up at sites which have ranked lists…like Ranker. As such, a lot of Ranker’s future growth is likely to parallel the growth of mobile and the growth of searches for opinion based questions.

- Ravi Iyer

Comments

comments

Combining Preferences for Pizza Toppings to Predict Sales

The world’s most expensive pizza, auctioned for $4,200 as a charity gift in 2007, was topped with edible gold, lobster marinated in cognac, champagne-soaked caviar, smoked salmon, and medallions of venison. While most of us prefer (or can only afford to prefer) more humble ingredients, our preferences are similarly diverse.  Ranker has a Tastiest Pizza Toppingslist that asks people to express their preferences. At the time of writing there are 29 re-ranks of this list, and a total of 64 different ingredients mentioned. Edible gold, by the way, is not one of them.

Equipped with this data about popular pizza toppings, we were interested in finding out if pizzerias were actually selling the toppings that people say that they want. We also wanted to see if we could predict sales for individual ingredients by looking at one list that combined all of the responses about pizza topping preferences. This “Ultimate List” contains all of toppings that were listed in individual lists (known as re-ranks) and is ordered in a way that reflects how many times each ingredient was mentioned and where they ranked on individual lists. Many of the re-ranks only list a few ingredients, so it is fitting to combine lists and rely on the “wisdom of the crowd” to get a more complete ranking of many possible ingredients.

As a real-world test of how people’s preferences correspond to sales, we used Strombolini’s New York Pizzeria’s list of their top 10 selling ingredients. Pepperoni, cheese, sausage and mushrooms topped the list, followed by: pineapple, bacon, ham, shrimp, onion, and green peppers. All of these ingredients, save for shrimp, are included in the Ranker lists so we considered the 9 overlapping ingredients and measured how close each user’s preference list was to the pizzeria’s sales list.

To compare lists, we used a standard statistical measure known as Kendall’s tau, which counts how many times we would need to swap one item for another (known as a pair-wise swap) before two lists are identical. A Kendall’s tau of zero means the two lists are exactly the same. The larger the Kendall’s tau value becomes, the further one list is from another.

The figure shows, using little stick people, the Kendall’s tau distances between users’ lists, and the Strombolini’s sales list. The green dot corresponds to a perfect tau of zero, and the red dot is the highest possible tau (if two lists are the exact opposite of the other). The dotted line is provided as a reference to show how likely each Kendall’s tau value is by chance (that is, how often different Kendall’s tau values occur for random lists of the ingredients). It is clear that there are large differences in how close individual users’ lists came to the sales-based list. It is also clear that many users produced rankings that were quite different from the sales-based list.

Using this model, the combined list came out to be: cheese, pepperoni, bacon, mushrooms, sausage, onion, pineapple, ham, and green peppers. This is a Kendall’s tau of 7 pair-wise swaps from the Strombolini list, as shown in the figure by the blue dot representing the crowd. This means the combined list is closer to the sales list than all but one of the individual users.

Our “wisdom of the crowd” analysis, combining all the users’ lists, used the same approach we previously applied to predicting celebrity deaths using Ranker data. It is a “Top-N” variant of the psychological approach developed in our work modeling decision-making and individual differences for ranking lists, and has the nice property of naturally incorporating individual differences.

This analysis is a beginning example of a couple of interesting ideas. One is that it is possible to extract relatively complete information from a set of incomplete opinions provided by many people. The other is that this combined knowledge can be compared to, and possibly be predictive of, real-world ground truths, like whether more pizzas have bacon or green peppers on them.  It may never begin to explain, however, why someone would waste champagne-soaked caviar on pizza, as a topping.

Comments

comments