The POOR-MAD approach: Preferred objects over rich, multi-attribute data

Preferences about objects of interest are often expressed at different levels of granularity, not always matching the level of detail of stored data. For instance, we prefer rock to pop music, yet scheduled concerts only cite the name of the performer, with no reference to the musical genre. In this paper, we address this common mismatch by leveraging the vast amounts of data organized in taxonomies (such as those found in electronic catalogs and classification systems). We present a model to represent preferences and state the desirable properties of preference propagation, such as the fact that more specific preferences always prevail over more generic ones. We then illustrate an approach for propagating preferences along taxonomies complying with the stated properties and show how the best objects can thereby be identified.