Skip to content

Archive for

Brain Pattern-based Recommender Systems–Coming Soon?

Now things are going to start to get real interesting! In The Learning Layer, I categorized the types of behaviors that could be used by recommender systems to infer people’s preferences and interests. The categories I described are:

  1.  Navigation and Access Behaviors (e.g., click streams, searches, transactions)
  2. Collaborative Behaviors (e.g., person-to-person communications, broadcasts, contributing comments and content)
  3. Reference Behaviors (e.g., saving, tagging, or organizing information)
  4. Direct Feedback Behaviors (e.g., ratings, comments)
  5. Self-Profiling and Subscription Behaviors (e.g., personal attributes and affiliations, subscriptions to topics and people)
  6. Physical Location and Environment (e.g., location relative to physical objects and people, lighting levels, local weather conditions)

Various subsets of these categories are already being used in a variety of systems to provide intelligent, personalized recommendations, with location-awareness being perhaps the most recent behavioral information to be leveraged, and the sensing of environmental conditions and the incorporation of that information in recommendation engines representing the very leading edge.

But I also described an intriguing, to say the least, seventh category–monitored attention and physiological response behaviors. This category includes the monitoring of extrinsic behaviors such as gaze, gestures, movements, as well as more intrinsic physiological “behaviors” such as heart rate, galvanic responses, and brain wave patterns and imaging. Exotic and futuristic stuff to be sure. But apparently it may not be as far off as one might think to actually be put into widespread practice, given this new advance in smart phone brain scanner systems.

Sure, it will take time for this type of technology to be cost effective, user-friendly, scale to the mass market, etc. But can there be any doubt that it will eventually play a role in providing information to our intelligent recommender systems?

Advertisements