Collaborative filtering (CF) is the process of predicting a user's interest in various items, such as books or movies, based on taste information, typically expressed in the form of item ratings, from many other users. One of the key issues in collaborative filtering is how to deal with data sparsity; most users rate only a small number of items. This paper's first contribution is a distance measure. This distance measure is probability-based and is adapted for use with sparse data; it can be used with for instance a nearest neighbor method, or in graph-based methods to label the edges of the graph. Our second contribution is a novel probabilistic graph-based collaborative filtering algorithm called PGBCF that employs that distance. By propagating probabilistic predictions through the user graph, PGBCF does not only use ratings of direct neighbors, but can also exploit the information available for indirect neighbors. Experiments show that both the adapted distance measure and the graph-based collaborative filtering algorithm lead to more accurate predictions.

We present a system towards the integration of data mining into relational databases. To this end, a relational database model is proposed, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules and decision trees, can be represented and queried using a unifying framework.

We present a prototype of an inductive database. Our system enables the user to query not only the data stored in the database but also generalizations (e.g. rules or trees) over these data through the use of virtual mining views. The mining views are relational tables that virtually contain the complete output of data mining algorithms executed over a given dataset. The prototype implemented into PostgreSQL currently integrates frequent itemset, association rule and decision tree mining. We illustrate the interactive and iterative capabilities of our system with a description of a complete data mining scenario.