The recommender industry 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. The three elements include:
- An effective model that relates the needs visitors have to what the business offers
- Quality data to build a model instance that relates specific needs to specific offerings
- Unobtrusive means for easily and quickly determining an individual user’s need
Note that these three components are not 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 rectify 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.
– Dr. Rick Hangartner, Chief Scientist, MyStrands, has nearly 30 years experience developing computing hardware and software in the aerospace, data communications, heavy-trucking, and supercomputing industries.
With recommendations becoming a thing of necessity among product websites, our algorithm has been able to deduce and deliver recommendations through website analytics data. Any product websites without a recommendation feature can just incorporate Plumb’s website analytics script and roll out a recommendation widget on their site.