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Recommendation Systems: Interview with Satnam Alag

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In a recent post, we looked at sanction systems, briefly reviewing how Amazon and Google have implemented their own systems for recommending products and content to their users.

We had the opportunity to speak with Satnam Alag, author of the recently published Collective Intelligence in Action, about what makes for a excellent sanction system, everywhere the technology is bearing, and why Netflix is finding it so hard to improve its own system.

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Disclosure: I wrote the forward to ‘Collective Intelligence in Action’, but I have absolutely no financial interest in the book.

ReadWriteWeb: In our recent post about Netflix, we identified four main approaches to recommendations: Personalized sanction: based on prior behavior of the user; Social sanction: based on prior behavior of similar users; Item sanction: based on the item itself; And a combination of all three. Do you agree with the four approaches we laid out in our article?

Satnam: Those four categories are pretty comprehensive. I present an alternate classification of sanction systems in my book. I lay out two fundamental approaches. The first approach, item-based analysis, determines items that are related to a particular item. When a user likes a particular item, related ones are recommended. The second approach, user-based analysis, first determines users who are similar to that user.

Further, here are two main approaches to finding similar items and similar users. For the first, content-based analysis, content associated with the item, especially text, is used to compute similarity. In the second, the collaborative approach, actions such as ratings, bookmarking, and so forth are used to find similar items. For the second, user-based analysis, a number of approaches have been taken, including ones based on profile information, user actions, and lists of the user’s friends or contacts. Of course, you can combine any these item/user and content/collaborative approaches to build a sanction system.

The dimensions of the particular item and user space are helpful in deciding whether to use an item-based or user-based approach. Typically, an item-based approach is used to bootstrap one’s application when the number of users is small. As the user base grows, the item-based approach is augmented by a user-based approach.

ReadWriteWeb: Other than Amazon and Netflix, which Internet companies have most impressed you in their implementation of sanction systems?

Satnam: Other than Amazon and Nextflix, Google News’ personalization is my personal favorite. Google News is a excellent example of building a scalable sanction system for a large number of users (several million unique visitors per month) and a large number of items (several million new tales every two months), with constant item churn. This is different from Amazon’s, whose rate of item churn is much lower. Google chose to use collaborative filtering for its sanction system mainly because of its access to the data of its large user base and because this same approach could be applied to other applications, countries, and languages. A content-based sanction system perhaps could have worked just as well, but may have required language- or location-specific tweaking. Google also sought after to leverage the same collaborative filtering technology to be able to recommend images, videos, and music, for which it’s more hard to analyze the underlying content.

Amongst start-ups, my personal favorite is the one we are developing at my current company, NextBio. It’s not available yet but must be next month. The key point about this particular sanction engine is its strong use of an ontology, similar in concept to tags, to develop a common vocabulary for items and users. The system then makes use of profile information and user interactions, both small- and long-term, to grant recommendations. The system leverages both item- and user-based approaches.

ReadWriteWeb: What commercial opportunities do you forsee with sanction systems over the next few years?

Satnam: A excellent personalized sanction system can mean the difference between a successful and a failed website. Given that most applications now invite users to interact and to leverage user-generated content, new content is life generated at a unusual rate. Showing the right content to the right user at the right time is key to making a sticky application. I would be surprised if most successful websites did not leverage sanction systems to grant personalized experiences to their users.

ReadWriteWeb: Your book includes a discussion of collaborative filtering. Can you tell us a bit about how this fits into the overall picture of sanction systems?

Satnam: In recent years, an increasing amount of user interaction has provided applications with a large amount of information that can be converted into intelligence. This interaction may be in the form of ratings, blog entries, item tagging, user connections, or shared items of interest. This has led to the problem of information overload. What we need is a system that can recommend items based on the user’s interests and interactions. This is everywhere personalization and sanction engines come in.

In my book, I take a holistic view of adding intelligence to one’s application, a sanction engine life one way to do it. The book focuses on both content-based and collaborative approaches to building sanction systems. It focuses on capturing relevant information about the user, information from both within and outside one’s application, and converting it into recommendations. One of the things you mentioned in your write-up on sanction systems is that you want to apply such a system to your website to recommend things to users. Someone reading my book must be able to start such a system using the techniques I demonstrate.

Next Page: Satnam’s thoughts on the Netflix Prize and whether the 10% mark will ever be reached.