Recommender systems are software tools and techniques providing suggestions and recommendations for items to be of use to a user. These sug- gestions can help users make better decisions on choosing products or services, such as which film to watch, what music to listen to or which travel insurance to buy. When making suggestions, many recommender systems do not consider contextual information, such as location or time [5]. Recommender systems that make use of contextual information are called context-aware recommender systems. Many context-aware recommender systems can not generate reliable rec- ommendations on sparse data. Besides, in most context-aware recommender systems, the contexts are pre-defined and not personalised. These limitations of existing methods usually lead to inaccurate recommendations. In this thesis, new context-aware recommendation methods are presented. In these methods, personalised contexts are defined based on users’ activity patterns. The underlying associations between contexts are analysed, and similar contexts are combined so that the system can make use of existing data collected in similar contexts. Experimental results from two datasets show that the proposed methods can achieve significantly higher recommendation accuracy than existing context-aware recommendation methods.