Collaborative filtering (CF) is currently the most popular technique used in commercial recommender systems. Algorithms of this type derive personalized product propositions for customers by exploitingstatistics derived from vast amounts of transaction data.Traditionally, basic CF algorithms have exploited a single category of ratings despite the fact that on many platforms a variety of different forms of user feedback are available for personalization and recommendation. In this paper we explore a collaborative feature-combination algorithm that concurrently exploits multiple aspects of the user model like clickstream data, sales transactions and explicit user requirements to overcome some known shortcomingsof CF like the cold-start problem for new users. We validate our contribution by evaluating it against the standard user-to-user CF algorithm using a dataset from a commercial Web shop. Evaluation results indicate considerable improvements in terms of user coverageand accuracy.