Collaborative filtering is the common technique of predicting the interests of a user by collecting preference information from many users. Although it is generally regarded as a key information retrieval technique, its relation to the existing information retrieval theory is unclear. This thesis shows how the development of collaborative filtering can gain many benefits from information retrieval theories and models. It brings the notion of relevance into collaborative filtering and develops several relevance models for collaborative filtering. Besides dealing with user profiles that are obtained by explicitly asking users to rate information items, the relevance models can also cope with the situations where user profiles are implicitly supplied by observing user interactions with a system. Experimental results complement the theoretical insights with improved recommendation accuracy for both item relevance ranking and user rating prediction. Furthermore, the approaches are more than just analogy: our derivations of the unified relevance model show that popular user-based and item-based approaches represent only a partial view of the problem, whereas a unified view that brings these partial views together gives better insights into their relative importance and how retrieval can benefit from their combination