by Rick Hangartner
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December 17, 2007 at 8:06 am
· Filed under Press, Strands, Technology
By Dr. Rick Hangartner, Chief Scientist, MyStrands. Guest column published in MSearchGroove
No, the headline on this entry is not a careless grammatical error. Nor is the question really “What is the recommender market?” That implies that “recommenders” are mature, well-defined technologies that deliver specific features and value to the online world. Emerging recommendation technologies are currently setting the standards for discovery and personalization in today’s social networking-dominated web 2.0 environment, and the future of online social networking is all about discovery and personalization. While search engines help you find things you know you are looking for, discovery helps you find the rest.
If we accept that every business must make its case in 10 to 20 seconds on its Web site, then we are all but forced to admit that recommenders, more than anything else, represent the conceptual answer to the question: “How can I get that visitor/user/customer to realize that I offer something of value to him or her?”
Although venture capitalists and Web 2.0 users may find that claim to be just the tiresome excuse they need for hitting the “Back” button, the point is that a good argument can be made that unlike search engines, the idea of recommenders is a formal concept that has as many different concrete examples as there are separate market applications. The recommender industry really is the business of pulling three components together into a system that helps a user-driven business convince their potential customers that they should stay for a while. These three elements include:
1) An effective model that relates the needs visitors have to what the business offers.
2) Quality data to build a model instance that relates specific needs to specific offerings.
3) Unobtrusive means for easily and quickly determining an individual user’s needs.
Note that these three components are not quite as simple as “good (statistical) algorithms,” “a lot of data,” or “simple user interfaces.” In the coming years, defining an effective model will increasingly involve a scientific approach to understanding user needs and the market strategy of the business. Gathering quality data will require more sophisticated understanding of which data is actually relevant to the model. Devising means for characterizing an individual user’s needs will depend on a refined understanding of how people implicitly and explicitly signal needs that they themselves may not even fully understand.
In short, the recommender industry is the evolving business of building and deploying systems that reify some of the psychology of human economic transactions. What this means for the marketplace seems relatively clear: Search engines as we know them will never disappear. In the near term, search engines will increasingly incorporate simple recommender technologies to handle approximate queries (e.g. “You asked for this, and based on similar queries/behavior by others, you might be looking for this.”). But in the long term, the recommender industry will be larger, and recommender technologies will be more pervasive than the search industry and search technology as we know it.
Beyond that, some general themes about the future of the recommender industry that seem to be worth watching for include:
Multiple revenue models: Unlike search engines, which primarily are monetized through contextual ads of some form, recommender systems will be monetized in multiple ways. Recommender technology suppliers will continue to partner with customer businesses to derive revenue as a share of explicit sales increases directly accredited to the recommender system. In the longer term, recommender technology will increasingly enable business models, including advertising schemes, which could not exist without it. An implicit valuation for a specific application of a recommender system will be derived from the enabled economic activity.
Increasing focus on how users require change over time: In that recommender systems reify aspects of the psychology of economic transactions, there is an increasing appreciation for the probable value of responding to how economic behavior changes over time. This includes how an individual’s needs change over time and how the needs of the community evolve. The former can, in part, be accommodated by simply taking care to build a recommender system instance using data that is an adequate sampling of individuals whose needs are changing. Adapting to the latter may also require recommender system models that explicitly incorporate features of how community needs to evolve.
New concepts of personalization: One of the recent trends in personalization is using information about an individual’s social network to better characterize that individual’s needs and interests. This may be just one aspect of a new concept of personalization that puts the focus not on delivering an isolating, customized experience to a person, but rather on connecting an individual with affinity communities who can provide information of value to that individual. Few people really want to be out there all alone. And for those explorers who do, they might, in reality, be hoping to build a community of like-minded souls or be waiting for others to catch up with them.
More than anything, the future of the recommender industry is a business that will continue to grow and become more sophisticated as the science of recommenders greatly develops and to increasingly encompasses computer science, psychology, economics and cognitive science.
Rick Turoczy of SiliconFlorist wrote an excellent treatise on life-streaming and said I’m going to use it [Strands] because it gives my lifestream value—and it gives me more valuable resources through participation.
Portland-based Marshall Kirkpatrick also wrote a thoughtful post on ReadWriteWeb with very good feedback. What we probably most enjoyed was the reference to the hot posts: hotness is hotness - right now you can view what the hottest items are in your whole circle of friends, in a particular group of friends or in the lifestream of one particular friend.
Jason Harris, the Salem-based blogger who writes for GigaOm, also took the time to review Strands.com. He let readers in on how we will be adding the ability to build a “taste profile” based on your social media usage patterns.
Mike Rogoway of The Oregonian also covered the launch. He explained how the company’s technology will direct people to new things that fit their tastes, and take their taste profiles to other sites.
(and wait for many new improvements and features!).
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