Privacy Issues

Another interesting application of this algorithm is for book recommendations. Consider a recommender system for library users. Each library user is encoded as a feature vector. The feature vector represents the books checked out by the user . Each element of the feature vector represents a small range of books within the spectrum of the Dewey Decimal Classification System (such as 100 103). This range represents books within the genre of philosophy. Upon application of the ART1 algorithm, clusters of feature vectors would be further refined to identify the percentage for which a particular book was checked out within the group . This data could be used to then recommend a book to a library user who may not be familiar with it.

This application actually works well with test data, but could never work practically due to privacy issues. Most libraries claim that they don't keep historical records of texts that a particular library user checks out. Without a historical data set, the ART1 algorithm in this application is useless.

There is also quite a bit of fear associated with personalization algorithms. When they work well, they have the ability to predict a user's actions. This can create an uneasy feeling for some as they realize that their behavior is being predicted solely upon external observations. While in this application, the result could be helpful to the user, other applications exist which can invade an individual's privacy. To combat the fear involved in these types of applications, most companies introduce privacy policies to define to customers what data is collected, what is done with it, and who is able to see or utilize it.