Visualization of Item Features, Customer Preference and Associated Uncertainty using Fuzzy Sets

Some of the requirements during preferences discovery or preference modeling through machine learning and data mining are: (i) understanding of features of items, e.g. genres content of a movie; (ii) understanding of patterns in customer feedback on items to explore and identify customer preferences; (iii) understanding of discovered patterns in customer preference on features of items, e.g. preference to genres of movies; and (iv) understanding of similarity among customers' preference, e.g. to form similar cluster of customers in their genre preference. An attempt is made to satisfy these requirements using fuzzy set driven information visualization technique; and movie as the item and genre as the feature are used for illustration. Visualization of features of items, patterns of customer previous feedback to these items, and the relationship between the feedback and item features along with associated measure of uncertainty are presented. The uncertainty is non-stochastic type that is induced from subjectivity, vagueness and imprecision in item features and user preference; and it is modeled using fuzzy set. The visualization of the discovered preference along various demographic features of the users is also presented. This in turn can help forming clusters of users with similar preference to various kinds of items.