Summary

This chapter has illustrated a simple algorithm that clusters sets of data into groups for use as a recommender system. While originally defined as an offline processing tool that could be used for data mining, the efficiency of the algorithm lends itself to real-time processing of data within a commercial Web environment.

The example application shown here was very simple and represented a very small data set. For Web site personalization, the data set could include not only a representation of the Web content, but also time spent viewing a particular piece of content. The types and representation of the data ultimately depend upon the algorithm providing the personalization service. With proper encoding within feature vectors, the ART1 algorithm can work with a wide variety of data representing many aspects of a consumer's behavior in a Web setting.

Despite the harmful applications that exist for personalization engines, they can be beneficial timesaving devices and therefore should not be overlooked.