This thesis describes work on using content to improve recommendation systems. Personalised recommendations help potential buyers filter information and identify products that they might be interested in. Current recommender systems are based mainly on collaborative filtering (CF) methods, which suffer from two main problems: (1) the ramp-up problem, where items that do not have a sufficient amount of meta-data associated with them cannot be recommended; and (2) lack of transparency due to the fact that recommendations produced by the system are not clearly explained. In this thesis we tackle both of these problems. We outline a framework for generating more accurate recommendations that are based solely on available textual content or in combination with rating information. In particular, we show how content in the form of social tags can help improve recommendations in the book and movie domains. We address the ramp-up problem and show how in cases where they do not exist, social tags can be automatically predicted from available textual content, such as the full texts of books. We evaluate our methods using two sets of data that differ in product type and size. Finally we show how once products are selected to be recommended, social tags can be used to explain the recommendations. We conduct a web-based study to evaluate different styles of explanations and demonstrate how tag-based explanations outperform a common CF-based explanation and how a textual review-like explanation yields the best results in helping users predict how much they will like the recommended items.