Mining context-related sequential patterns for recommendation systems

A typical recommendation system answers such questions as what are the interesting items for the current user. Most traditional recommendation systems have not taken the situational information into account when making recommendations, which seriously limits their effectiveness in the ubiquitous computing application environment, where a user´s request is generally related to, and a system´s response should be dependent on, a specified context (e.g., a specific place, time slot, noise level, or temperature range). In this paper we propose a context-aware recommendation approach to enhance the performance of recommenders. This approach is characterized by a novel sequential pattern mining algorithm that can efficiently mine and group patterns by context.