a collaborative constraint-based meta-level recommender
Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.
collaborative filtering hybrid recommendation approaches knowledge-based recommendation