--The Art, Science and Business of Recommendation Engines 【转帖】

http://www.readwriteweb.com/archives/recommendation_engines.php In October last year, Netflix500)this.width=500'> launched an unusual contest500)this.width=500'>. The online movie rental company is offering 1 million dollars to anyone who can improve their recommendation engine by 10%. Netflix is known for its innovation and bold moves500)this.width=500'> and in the grand scheme of things, $1M is not a lot of money for such a business. The competition is still running (it "continues through at least October 2, 2011"), so is this a publicity trick or an attempt to do research on the cheap? Is better recommendations something that Netflix really needs or is it just nice to have? Today Netflix is facing a challenge from the awakened giant BlockBuster, so it is certainly looking for a competitive edge. A great recommendation system can retain and attract users to the service. For example when a user returns a movie, he/she is recommended another movie they might like - which increases the likelihood of return business. Browsing and Recommendations A good recommendation engine can make a difference not just for Netflix, but for any online business. This is because there are two fundamental activities online - Search and Browse. When a consumer knows exactly what she is looking for, she searches for it. But when she is not looking for anything specific, she browses. It is the browsing that holds the golden opportunity for a recommendation system, because the user is not focused on finding a specific thing - she is open to suggestions. During browsing, the user's attention (and their money) is up for grabs. By showing the user something compelling, a web site maximizes the likelihood of a transaction. So if a web site can increase the chances of giving users good recommendations, it makes more money. Obviously this is a difficult problem, but the incentive to solve it is very big. The main approaches fall into the following categories: Personalized recommendation - recommend things based on the individual's past behavior Social recommendation - recommend things based on the past behavior of similar users Item recommendation - recommend things based on the thing itself A combination of the three approaches above We will now explore these different approaches by looking at old-timers like Amazon and newbies like Pandora and del.icio.us. Amazon - The King of Recommendations Amazon is considered a leader in online shopping and particularly recommendations. Over the last decade the company has invested a lot of money and brain power into building a set of smart recommendations that tap into your browsing history, past purchases and purchases of other shoppers - all to make sure that you buy things. Lets take a look at various pieces of Amazon's recommendation system to get an insight on how they work. Here are the sections that are shown in the main area of my Amazon account when I login: 500)this.width=500'> The section above shows Social recommendations. Notice that it is very analytical, giving me a statistical reason for why I should buy this item. Also note that this recommendation is also a Personalized recommendation, since it is based on an item that I clicked recently. 500)this.width=500'> The section above shows Item recommendation based on New Releases. Clicking on the Why is this recommended for you? link takes me to a view of my purchasing history. So this recommendation is also a Personalized recommendation, since it is based on my past behavior. There are four more sections offered on the page and each of them leverages different combinations of the personalization mechanisms described above. We summarize them in the table below: 500)this.width=500'> Not surprisingly, the system is symmetric and comprehensive. All recommendations are based on individual behavior, plus either the item itself or behavior of other people on Amazon. Whether you like to buy something because it is related to something that you purchased before, or because it is popular with other users, the system drives you to add the item to the shopping cart. Beyond Amazon The Amazon system is phenomenal. It is a genius of collaborative shopping and automation that might not be possible to replicate. This system took a decade for Amazon to build and perfect. It relies on a massive database of items and collective behavior that also "remembers" what you've done years and minutes ago. How can new companies compete with that? Surprisingly, there is a way. The answer is found in a subject that has little to do with online shopping - genetics. As you know, this science studies how pieces of DNA, called genes, encode human traits and behavior. For example, members of a family look and behave alike because they share a certain subset of genes. Genetics as a science has been around for over 150 years and has been a powerful tool for both medicine and history. But on January 6, 2000 things took an unexpected turn - Tim Westergren and his friends decided to apply the concepts of genetics to music. Pandora - The Recommendation System Based on Genetics The Music Genome Project500)this.width=500'> was launched to decompose music into its basic genetic ingredients. The idea behind it is that we like music because of its attributes - and so why not design a music recommendation system that leverages the similarities between pieces of music. This kind of recommendation engine falls into the Item recommendation category. But what is new and profound here is that similarity of an item like a piece of music needs to be measured in terms of its "genetic" make up. 500)this.width=500'> After years of struggle and processing massive amounts of music, the project accumulated enough data and launched the service called Pandora500)this.width=500'>. Pandora became a hit because of its precision and low cost of entry. The user just needs to pick one artist, or a song, to create a station that instantly plays similar music. This kind of instant gratification is difficult to resist. The fact that Pandora understands what makes music similar allows it to hook the user without having to learn what this user likes. Pandora does need the user's tastes or memory, it has its own - based on music DNA. Sure, sometimes it might not be perfect, as the user's taste might not be perfectly addressed. But it is rarely wrong. The natural question is can this genes-based approach be applied to other areas - like books, movies, wines, restaurants or travel destinations? What constitutes genes for each category? For example, can we say that for wine, the genes might be things that describe how wine tastes: blackberry, earthy, fruity, complex, blend, etc. And for a book, can the genes be phrases that describe the plot? So if the genes are the attributes of the object that make it unique in our mind, we should have no problem coming up with genes for various things. In the past few years we have been doing this a lot online. It's called tagging! Del.icio.us - Can Tags Become Genes? Pandora had a big startup cost, because thousands of pieces of music had to be manually annotated. The social bookmarking phenomenon del.icio.us500)this.width=500'> took a different approach - let people annotate things themselves. This self-organizing approach has worked really well, and del.icio.us quickly became popular among early adopters. Today, del.icio.us is considered to be more than bookmarking destination - it is also a news site and a search engine. But is del.icio.us a recommendation system? 500)this.width=500'> The answer is yes. There is a basic recommendation system based on one gene - a single tag. For example, in the picture above we see popular links for the linux tag and we also see related tags like open source and ubuntu. But a much more exciting recommendation system is based on matching multiple tags. Unfortunately, the current heuristic does not always work, which is why it is not obvious. But luckily, it did work for the Read/WriteWeb page and generated a great list of similar blogs (see "related items" below): 500)this.width=500'> So the del.icio.us approach holds intriguing possibilities of self-organizing classification and recommendation systems. With enough users and more tweaking, social tagging can result in a system that works equally well for books, wine and music. Provided, of course, that tags are so good that they become genes! Conclusion Recommendation engines are important pieces of online commerce systems and their user experience. Retailers have a big incentive to provide recommendations to those users who are "just browsing", to drive them towards a transaction. Amazon.com, the leader in the space, has a very compelling personalization offering. The problem that other retailers face is lack of user information and infrastructure. Recent approaches to recommendation engines, like the genetics-inspired Pandora and social tagging pioneered by del.icio.us, are intriguing. These approaches hold the promise to provide instant gratification, without asking the user to reveal her preferences and past history. Regardless of how things unfold in the future, Amazon, Pandora and del.icio.us are examples of extraordinary recommendation technologies. We commend them and are watching in fascination for what is coming next. 500)this.width=500'> Audio / 500)this.width=500'> digg this / 500)this.width=500'> add to del.icio.us / 500)this.width=500'> stumble it / 500)this.width=500'> Reddit it / Sphere It / 500)this.width=500'> Slashdot It!

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