Grade Pending

I recently have switched from a Blackberry to an iPhone for my personal phone.  I do, however, still prefer Blackberry for corporate use.  Since my introduction to iPhone, I have been paying a lot more attention to mobile apps and how they can help us get through our every day life.

Several years ago NYC decided to grade restaurants based on sanitation.  My ex-coworker has come up with an iPhone app that allows you to figure out the grade of a restaurant even if the sign says "grade pending".  This is yet another example of a useful piece of technology.

Download Dan's app here.

The database that this app runs off of from the NYC Department of Sanitation might be a good database for FoodNOW to start with?

Foodspotting

In early 2011, Dave M informed me about this food website, foodspotting.com in doing some research for my foodNOW idea.  What makes this foodie website unique is that it focuses on dishes and not reviews of specific restaurants.  "Foodspotting is the easiest way to find and share the foods you love: Instead of reviewing restaurants, you can recommend your favorite dishes and see what others have recommended wherever you go."  I recently came across them again when reading Inc. magazine's 30 under 30 piece where the company was profiled.  What has caught my eye is that this year they want "[to] become as smart-as-Pandora recommendation engine for nearby food."  Another thumbs up for the original idea owner, me!

An intellectual curiosity, curated content…

The need for intelligent filtering
As technology continues to improve, more and more people will begin to feel the effects of information overload. We are bombarded by content from all directions and most of the time we have no idea where to look. We have no idea what is or is not significant. Out of this phenomenon emerges an important trend to watch in 2011—how we apply intelligent filtering through artistic “curation.” Historically, the term has not been used outside of the art world. That said, welcome to the age of the digital curator. One of the most critical roles a business can play in today’s information society is to lend a helping hand in the process of intelligent filtering.

What’s the buzz around curated content?
Content curation is mastering the art of sourcing and sharing only the best ideas. The digital curator participates in the act of finding, organizing and presenting the most valuable content on any specific issue. This is a powerful concept because curation does not focus on creating new content, but does focus on sharing what content is significant. This emerging media space is where opinion leaders will continue to provide and prove their added value.

Models for content curation
Content curation can be broken down into micro-activities:
• Amalgamation: presenting information in a single location
• Refinement: conveying content in a more simplistic format
• Distillation: finding insights within a collective data set
• Mashup: mixing content to create a new point of view
• Chronology: organizing information sequentially to show an evolving understanding

Curating companies and services
Many of the world’s top businesses have adopted and successfully monetized the curation of content. Amalgamation, refinement and distillation are at the heart of Google’s product offering. Millions of people use google.com as their digital curator. Sites like wolframalpha.com (whose goal is to make all systematic knowledge immediately computable and accessible to everyone) and hunch.com (whose computer algorithms hope to provide highly-customized recommendations) have followed in Google’s footsteps. At this year's SXSW festival, Ogilvy debuted a visual notes service called Ogilvy Notes that refines ideas into pictures. Last year, Bloomberg launched a real-time financial news “mashup” service called First Word which is the “go to spot” for Wall Street traders who want short, succinct, and relevant company news. Smart brands understand this process of intelligent filtering and those that adopt models for content curation will likely win more trust and attention from their customers.

Project foodNOW

foodNOW is an idea born out of my love for food and technology.  It is a mobile phone application idea that allows people to enter a restaurant that they enjoy and then foodNOW would respond by outputting restaurant suggestions that are similar. Users would also be able to provide feedback on approval or disapproval of restaurant suggestions, which the foodNOW application would then take into account for future selections. foodNOW is predictive and interactive.

Let’s take New York City as an example, even though this mobile phone application could be applied to any city. Imagine going to your favorite restaurant only to find that there is an hour wait. What are you to do now that you aren’t in front of your computer and don’t have Menupages or Citysearch at your finger tips? The answer is foodNOW.

Let’s say the restaurant that you are trying to go to is Coffee Shop in Union Sq so you type this into foodNOW. It will quickly scan its entire database of restaurants to find a restaurant with similarities to your choice. Taking into consideration proximity to your current location, foodNOW returns Cafeteria and outputs the restaurant’s address, opening hours, telephone number and website information. foodNOW understands that if you like Coffee Shop you will most likely enjoy Cafeteria (which is just around the corner). You now instantaneously have the information to call Cafeteria and make a reservation or most likely, find out how long the wait is there. If the restaurant is not quite right you can tell it so next time its predictions will get better for you.

Storyboard pictures of the PowerPoint presentation are below:

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OpenTable Mobile App

Seems like OpenTable has a heavily used mobile phone app. 5 million diners were seated via a mobile phone app. Here we have another good example of technology improving everyday life!

Read the press release on OpenTable's website.

Web of Mouth 2.0, an Exploration of Trust through Online Restaurant “Guidebook” Communities

This dissertation was submitted to the Department of Media and Communications, London School of Economics and Political Science, 2010, in partial fulfillment of the requirements for the MSc in Media and Communications.

If you would like to read the 12,000 research paper in its entirety please get in touch with me!

Abstract
This is a study of online food and restaurant recommendation websites and how we come to use or not use them in our decision making process. By employing a media studies audience uses and gratification approach combined with a marketing research word of mouth perspective this body of research seeks to understand the motivations, behaviors and consequences of use surrounding online restaurant recommendation websites and the pivotal role of trust in their feedback mechanisms. This paper provides insights into how the internet is influencing trust habits and examines whether or not electronic word of mouth is successful in the context of online food and restaurant recommendation websites such as Yelp, Chowhound, OpenTable, MenuPages, and New York Magazine. It studies why opinion seekers are willing to accept online customer reviews by asking: why do people trust (or distrust)online restaurant recommendations? Through a combination of 21 short surveys and interviews I discovered that respondents generally trust the electronic word of mouth for restaurant recommendations. There are certain attributes and cues that signal which online restaurant recommendations are most useful. How well a review is written has the most impact on credibility and usefulness for respondents; interviewees also use comprehensiveness as an indicator of trustworthiness. Within our sample, attribute centric electronic word of mouth restaurant recommendations were considered the most beneficial. Results also indicate the formation of a new kind of parasocial trust. Furthermore, the traditional word of mouth is moving online as demonstrated by the appropriation of guidebook communities into our respondents’ restaurant decision making processes.

hunch.com

So hunch.com "was started by clever folks who were exploring how machine learning could be used to guide practical, smart, and highly-customized recommendations."

This proves that that foodNOW was a good idea and still is a good idea...

Hunch gives customized recommendations and gets smarter the more you use it. This is internet social capital at its best!

Good job guys and keep the recommendations coming...

Pandora applied to foodNOW

What is the technology behind Pandora? How does Pandora work?

The Music Genome Project

Wikipedia's description: The Music Genome Project, created in January 2000, is an effort founded by Will Glaser, Jon Kraft, and Tim Westergren to "capture the essence of music at the fundamental level" using over 400 attributes to describe songs and a complex mathematical algorithm to organize them. The company Savage Beast Technologies was formed to run the project. A given song is represented by a vector (a list of attributes) containing approximately 150 "genes" (analogous to trait-determining genes for organisms in the field of genetics). Each gene corresponds to a characteristic of the music, for example, gender of lead vocalist, level of distortion on the electric guitar, type of background vocals, etc. Rock and pop songs have 150 genes, rap songs have 350, and jazz songs have approximately 400. Other genres of music, such as world and classical, have 300–500 genes. The system depends on a sufficient number of genes to render useful results. Each gene is assigned a number between 1 and 5, in half-integer increments.

foodNOW

So why can't the same framework be used to describe restaurants? 400 attributes are not necessary, but more than 4 categories (food, service, price and atmosphere) would be necessary to create a useful vector. Comments?

Possible attributes:
-price
-value
-location
-type of food
-type of place
-chef
-trendy
-chain
-take out
-hours
-delivery
-desserts
-liquor diversity
-wine list
-ratings from other sites

This concept can take on many permutations. It would be amazing if the technology could store menus where users could rate their favorite dishes. For example I love carrot cake, but what restaurants in New York serve good carrot cake? Menupages has a function to search by type of food. The website returned a list of restaurants that serve carrot cake, but still ranked via the 4 original categories. Should I assume that because a restaurant is rated well that the place has good carrot cake? No, I cannot come to this conclusion without reading through countless reviews that may or may not talk about dessert. So I what would like what my application to allow you to put in a specific food where it then would provide suggestions on where to find it!

My big idea could offer users an accurate cross-content and cross platform search by taste engine.

Bloomberg Function Comparision

I found a version of my idea at work. Another parallel can be drawn to a function that I use in Bloomberg called COMB. In finance we use COMB to determine a bond's relative value as compared to other bonds with similar maturities, ratings, currencies, and industry classifications. COMB use a default search to find comparable bonds and displays the current price, yield, and spreads for each security.

So basically in English this means when I have a bond load into the system I can then ask Bloomberg to search though thousands of other bonds to find a similar bond. This function is like a thesaurus for bonds. Note, one does not have to modify the filter for the search to be successful. Bonds are more like words, while restaurants have more song like qualities, ie. there is some qualitative aspect that needs interpretation.

How does a computer predict behavioral patterns?

I was speaking to my father about my idea this morning and he was telling me how he can relate to this in his field, science. He said, "In medical science we do computer modeling. We have a bunch of laboratory data that is associated with a clinical out come. So we collect samples from a statistically significant number of patients usually in the hundreds to thousands and train the computer to learn that a laboratory parameter is associated with a clinical outcome. Then we collect another set of samples usually in ten to hundred thousands and actually do the analysis asking whether that lab parameter is associated with that clinical outcome. You collect enough preliminary preferences through his/her usage and the computer learns his/her preferences. You do this for thousands and thousands of users and you can statistically predict that an individual will select a certain choice. Thus you have come up with a formula for predicting success."

Although this is an interesting concept, collecting enough data so you can predict a future pattern, it is different than my initial concept for foodNOW. foodNOW should already have a "formula" for predicting whether a person would like a restaurant based on his/her comparable choice. Of course this "formula" or technology should by dynamic and take user preferences to improve upon future suggestions.

Naive Bayes Classifier

I was told that I should explore Bayesian statistics in trying to further understand how a computer can learn. This seems like another way to classify or code a data set.

My go to site Wikipedia explains, "A naive Bayes classifier is a term in Bayesian statistics dealing with a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".

In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter. Even though these features depend on the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.

Depending on the precise nature of the probability model, naive Bayes classifiers can be trained very efficiently in a supervised learning setting. "

Ingenious Genius Feature

Apple has a new feature in iTunes which is an instant playlist. Elliot Feldman of the Associate Press explains, "Genius is a feature of iTunes 8 that creates instant playlists from the music that's in your iTunes Library. Just select a song to play from your iTunes Library, click on the Genius button (an atomic symbol) on the bottom row of the iTunes 8 browser interface, and an instant playlist of songs is generated from your iTunes Library. Along with the instant playlist generated from your iTunes Library, Genius also generates a sidebar list of compatible songs that you can purchase from the online iTunes Store."

Now how does this work? I was told that they cross-reference data from everyone's playlists so if a lot of people like Madona and they also like Britney Spears then iTune's uses this data to make the Genius playlist. A verision of Bayesian statistics at work here? Now this might be the cheaper and only way data about restaurants can be gathered because unlike songs, restaurants constantly change. So is a "restaurant genome project" not possible? Just something to think about here.

Google Maps

I downloaded google maps the other day and found out about a technology called my latitude. It allows you to "See your friends' locations and status messages and share yours with them." This could be considered a invasion of privacy, but indeed it is a useful tool. It apparently uses cell phone towers to approximate your location. Google maps gave my cell phone GPS functions without having GPS. Now, I wonder if it is possible for foodNOW to be linked to or connect to the google maps function or have this function embedded in the application itself. This might be asking too much of one little application.

Mobile Social Networking Users

eMarketer forecasts that mobile social networking will grow from 82 million users in 2007 to over 800 million worldwide by 2012.

Clearly this proves that the user base is there and growing quickly. If the correct application is developed I think I could be well positioned to capitalize off this growth in mobile social networking.













The target market is available and only growing!

Naming...

I have not given much thought to the name of this technology, my "Restaurant Genome Project".

-Foodengineer
-Foodengine
-Food + Predict
-Food + Guess
-Food for thought.
-iChow (there is already mychow on chowhound)
-foodNOW (this might be the best so far?)
-FeedMe
-Food + Thesaurus = foodasaurus

Big Idea!

One day this idea just hit me and I called up my friend Andy to tell him that I thought of a great idea. He is my go to entrepreneur friend that would appreciate something like this. My great idea was Pandora except for restaurants. Initially I thought this would be a good website, but there are already so many out there in this space--just to name a few Citysearch, Menupages and Urban Spoon. It is worth mentioning that none of those sites are really are predictive in nature. I am not sure how Pandora works, but if this could be done with restaurants and not songs, I know the application (whether a website or mobile phone application)would be successful because it solves a common problem that everyone has.

As we conversed more, the idea got better. I wanted to develop an iPhone application that would combine the Urban Spoon iPhone app + Pandora.