recommendation engines

Weekly Round-Up: Data Science Metro Map, Big Data Workers, Prescriptive Analytics, and Knewton

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from big data workers to educational recommendation algorithms. In this week's round-up:

  • Becoming a Data Scientist – Curriculum via Metromap
  • The Growing Need for Big Data Workers: Meeting the Challenge With Training
  • How Prescriptive Analytics Could Harness Big Data to See the Future
  • Q&A With Knewton’s David Kuntz, Maker of Algorithms

Becoming a Data Scientist – Curriculum via Metromap

For those of you looking to get started learning data science but don't know where to begin, this blog post literally maps it out for you. The author has taken the broad subject of data science and created a train map similar to those found in all major cities with public transportation. The different tracks of data science are depicted as different color train lines in the map and the subjects within those tracks are depicted as stops along those lines. Very interesting and definitely worth a look!

The Growing Need for Big Data Workers: Meeting the Challenge With Training

This is a Wired article about how the need for big data workers is growing as there is more and more data that needs to be collected, organized, analyzed, and acted upon. The article talks about the challenges of educating people and highlights the efforts of a few companies such as IBM, Big Data University, and DeveloperWorks.

Speaking of data science education, Data Community DC is hosting a Natural Language Processing Basics workshop on July 27th and there are still a few seats left. You can view details and sign up here.

How Prescriptive Analytics Could Harness Big Data to See the Future

Our third piece this week is about prescriptive analytics and how organizations can use it to help them make data-driven improvements in their operations. The article defines prescriptive analytics, contrasts it with the more commonly used descriptive and predictive analytics, and provides some examples as to how it can be useful.

Q&A With Knewton’s David Kuntz, Maker of Algorithms

Our final piece this week is an article about a company call Knewton and the interesting work they do. Knewton designs recommendation systems for educational products, which help customize the learning experience and tailor it to the individual student. In this article the author interviews David Kuntz, who is Knewton's Vice President of Research, about how their technology works, what kinds of things it can do, and what this means for education in the future.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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The State of Recommender Technology

Reblogged with permission from Cobrain. socialnetwork_graph

So let’s start with the big idea that is the reason that we are all here: recommendation engines. If you are reading this, you have probably already overcome the mental hurdle of the massive design and implementation challenge that recommendation engines represent, otherwise I can’t imagine why you would have signed up! Or perhaps you don’t know what a massive design and implementation challenge recommendation engines represent. Either way, you’re in the right place- this post is an introduction to the state of the technology of recommendation systems.

Well sort of– here is a working state of the technology: Academia has created a series of novel machine learning and predictive algorithms that would allow scarily accurate trend analysis, recommendations, and predictions given the right, unbiased supervised training sets of sufficient magnitude. Commercial applications in very specific domains have leveraged these insights and extremely large data sets to create interesting results in the release phase of applications but have found that over time the quality of these predictions decreases rapidly. Companies with even larger data sets that have tackled other algorithmic challenges involving supervised training sets (Google) have avoided current recommender systems because of their domain specificity, and have yet to find a generic enough application.

To sum up:

Recommendation Engines are really really hard, and you need a whole heckuva lot of data to make them work.

Now go build one.

Don’t despair though! If it wasn’t hard, everyone would be doing it! We’re here precisely because we want to leverage existing techniques on interesting and novel data sets, but also to continue to push forward the state of the technology. In the process we will probably learn a lot and hopefully also provide a meaningful experience for our users. But before we get into that, let’s talk more generically about the current generation of recommender systems.

Who Does it Well?

The current big boys in the recommendation space are AmazonNetflixHunch (now owned by eBay), Pandora, and Goodreads. I strongly encourage you to understand how these guys operate and what they do to create domain specific recommendations. For example, the domain of Goodreads, Netflix, and Pandora is books, movies, and music respectively. Recommending inside a particular domain allows you to leverage external knowledge resources that either solve scarcity issues or allow ontological reasoning that can add a more accurate layer on top of the pure graph analyses that typically happen with recommenders.

Amazon and Hunch seem to be more generic, but in fact they also have domain qualification. Amazon has the data set of all SKU-level transactions through it’s massive eCommerce site. Even so, Amazon has spent 10 years and a lot of money perfecting how to rank various member behaviors. Because it is Amazon-specific, Amazon can leverage Amazon-only trends and purchasing behaviors, and they are still working on perfecting it. Hunch doesn’t have an item-specific domain, but rather a system-specific domain, using social and taste-making graphs to propose recommendations inside the context of social networks.

Speaking of Amazon’s decade long effort to create a decent recommender with tons of data, I hope you’ve heard of the Netflix Prize. Netflix was so desperate for a better algorithm for recommendations that they instituted an X-Prize like contest for a unique algorithm for recommending movies in particular. In fact, the test methodology for the Netflix Prize has become a standard for movie recommendations, and since 2009 (when the prize was awarded) other algorithm sets have actually achieved better results, most notably, Filmaster.

Given what these companies have tried to do, we can more generically speak of the state of the technology as follows: An “adequate” recommender system comprises of the following items:

  1. An unbiased, non-scarce data set of sufficient size
  2. A suite of machine learning and predictive algorithms that traverse that data set
  3. Knowledge resources to apply transformations on the results of those algorithms

Pandora is a great example of this. They have created an intensive project at detailing a “music genome” or an ontological breakdown of a sample of music. The genome itself is the knowledge resource. The analysis of the genomics of a piece of music aggregated across a large number of pieces is the unbiased non-scarce data set of sufficient size. Finally the suite recommendation algorithms that Pandora applies to these two sets then generates ranked recommendations that are interesting.

Types of Recommenders

Without getting into a formal description of recommenders, I do want to list a few of the common types of recommendation systems that exist within domain specific contexts. To do this, I need to describe the two basic classes of algorithms that power these systems:

  1. Collaborative Filtering: recommendations based on shared behavior with other people or things. E.g. if you and I bought a widget, and I also bought a sprocket, it is likely that you would also like a sprocket.
  2. Expert Adaptive or Generative Systems: recommendations based on shared traits of people or things or rules about how things interact with each other in a non-behavior way. E.g. if you play football and live in Michigan, this particular pair of cleats is great in the snow.

In the world of recommenders, we are trying to create a semantic relationship between people and things, therefore we can discuss person-centric and item-centric approaches in each of these classes of algorithms; and that gives us four main types of recommenders!

  1. Personalized Recommendations- A person-centric, expert adaptive model based on the person’s previous behavior or traits.
  2. Social/Collaborative Recommendations- A person-centric collaborative filtering model based on the past behavior of people similar to you, either because of shared traits or shared behavior. Note that the clustering of similar people can fall into either algorithm set, but the recommendations come from collaborative filtering.
  3. Ontological Reasoned Recommendations- An item-centric expert adaptive system that uses rules and knowledge mined with machine learning approaches to determine an inter-item relational model.
  4. Basket Recommendations- An item-centric collaborative filtering algorithm that uses inter-item relationships like “purchased together” to create recommendations.

Keep in mind, however, that these types of recommenders and classes are very loose and there is a lot of overlap!

Conclusion

Now that large scale search has been dramatically improved and artificial intelligence knowledge bases are being constructed with a reasonable degree of accuracy, it is generally considered that the next step in true AI will be effective trend and prediction analysis. Methodologies to deal with Big Data have evolved to make this possible, and many large companies are rushing towards predictive systems with a wide range of success. Recent approaches have revealed that near-time, large data, domain-specific efforts yield interesting results, if not truly predictive.

The overwhelming challenge is not just in engineering architectures that traverse graphs extremely well (see the picture at the top of this post), but also in finding a unique combination of data, algorithms, and knowledge that will give our applications a chance to provide truly scary, inspiring results to our users. Even though this might be a challenge, there are four very promising approaches that we can leverage within our own categories.

Stay tuned for more on this topic soon!

Weekly Round-Up: Industrial Internet, Business Culture, Visualization, and Beer Recommendations

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from the Industrial Internet to beer recommendations. In this week's round-up:

  • The Googlization Of GE
  • 10 Qualities a Data-Friendly Business Culture Needs
  • Interview with Miriah Meyer - Microsoft Faculty Fellow and Visualization Expert
  • Recommendation System in R

The Googlization Of GE

This is an interesting Forbes article about GE, the Internet of Things (which it calls the Industrial Internet), and how they are trying to be to that space what Google has become to the consumer data space.

10 Qualities a Data-Friendly Business Culture Needs

Running a data-driven organization requires not only having the right talent, tools, and infrastructure to meet the organization's objectives. It also requires a data-friendly culture, which is the premise for this article. The author identifies 10 qualities that can make for a better environment to foster innovative data-driven processes.

Interview with Miriah Meyer - Microsoft Faculty Fellow and Visualization Expert

This post is part of Jeff Leek's interview series on his Simply Stats blog. This week Jeff interviewed Miriah Meyer, who is an expert on data visualization. The interview includes questions about her work, background, influences, and advice she has for data scientists about visualization.

Recommendation System in R

This is a fun blog post about putting together a beer recommendation system using the R statistical programming language. The author walks us through the processes he followed, includes snippets of the code he used, and even shows off the resulting app where you choose a beer you like and it recommends other beers that are similar to it.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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Why You Should Not Build a Recommendation Engine

One does not simply build an MVP with a recommendation engine Recommendation engines are arguably one of the trendiest uses of data science in startups today. How many new apps have you heard of that claim to "learn your tastes"? However, recommendations engines are widely misunderstood both in terms of what is involved in building a one as well as what problems they actually solve. A true recommender system involves some fairly hefty data science -- it's not something you can build by simply installing a plugin without writing code. With the exception of very rare cases, it is not the killer feature of your minimum viable product (MVP) that will make users flock to you -- especially since there are so many fake and poorly performing recommender systems out there.

A recommendation engine is a feature (not a product) that filters items by predicting how a user might rate them. It solves the problem of connecting your existing users with the right items in your massive inventory (i.e. tens of thousands to millions) of products or content. Which means that if you don't have existing users and a massive inventory, a recommendation engine does not truly solve a problem for you. If I can view the entire inventory of your e-commerce store in just a few pages, I really don't need a recommendation system to help me discover products! And if your e-commerce store has no customers, who are you building a recommendation system for? It works for Netflix and Amazon because they have untold millions of titles and products and a large existing user base who are already there to stream movies or buy products. Presenting users with  recommended movies and products increases usage and sales, but doesn't create either to begin with.

There are two basic approaches to building a recommendation system: the collaborative filtering method and the content-based approach. Collaborative filtering algorithms take user ratings or other user behavior and make recommendations based on what users with similar behavior liked or purchased. For example, a widely used technique in the Netflix prize was to use machine learning to build a model that predicts how a user would rate a film based solely on the giant sparse matrix of how 480,000 users rated 18,000 films (100 million data points in all). This approach has the advantage of not requiring an understanding of the content itself, but does require a significant amount of data, ideally millions of data points or more, on user behavior. The more data the better. With little or no data, you won't be able to make recommendations at all -- a pitfall of this approach known as the cold-start problem. This is why you cannot use this approach in a brand new MVP. 

The content-based approach requires deep knowledge of your massive inventory of products. Each item must be profiled based on its characteristics. For a very large inventory (the only type of inventory you need a recommender system for), this process must be automatic, which can prove difficult depending on the nature of the items. A user's tastes are then deduced based on either their ratings, behavior, or directly entering information about their preferences. The pitfalls of this approach are that an automated classification system could require significant algorithmic development and is likely not available as a commodity technical solution. Second, as with the collaborative filtering approach, the user needs to input information on their personal tastes, though not on the same scale. One advantage of the content-based approach is that it doesn't suffer from the cold-start problem -- even the first user can gain useful recommendations if the content is classified well. But the benefit that recommendations offer to the user must justify the effort required to offer input on personal tastes. That is, the recommendations must be excellent and the effort required to enter personal preferences must be minimal and ideally baked into the general usage. (Note that if your offering is an e-commerce store, this data entry amounts to adding a step to your funnel and could hurt sales more than it helps.) One product that has been successful with this approach is Pandora. Based on naming a single song or artist, Pandora can recommend songs that you will likely enjoy. This is because a single song title offers hundreds of points of data via the Music Genome Project. The effort required to classify every song in the Music Genome Project cannot be understated -- it took 5 years to develop the algorithm and classify the inventory of music offered in the first launch of Pandora. Once again, this is not something you can do with a brand new MVP.

Pandora may be the only example of a successful business where the recommendation engine itself is the core product, not a feature layered onto a different core product. Unless you have the domain expertise, algorithm development skill, massive inventory, and frictionless user data entry design to build your vision of the Pandora for socks / cat toys / nail polish / etc, your recommendation system will not be the milkshake that brings all the boys to the yard. Instead, you should focus on building your core product, optimizing your e-commerce funnel, growing your user base, developing user loyalty, and growing your inventory. Then, maybe one day, when you are the next Netflix or Amazon, it will be worth it to add on a recommendation system to increase your existing usage and sales. In the mean time, you can drive serendipitous discovery simply by offering users a selection of most popular content or editor's picks.