Developing Constraint-based Recommenders

Abstract

Traditional recommendation approaches (content-based filtering [48] and collaborative filtering[40]) are well-suited for the recommendation of quality&taste products such as books, movies, or news. However, especially in the context of products such as cars, computers, appartments, or financial services those approaches are not the best choice (see also Chapter 11). For example, apartments are not bought very frequently which makes it rather infeasible to collect numerous ratings for one specific item (exactly such ratings are required by collaborative recommendation algorithms). Furthermore, users of recommender applications would not be satisfied with recommendations based on years-old item preferences (exactly such preferences would be exploited in this context by content-based filtering algorithms).