Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity

Retrieving timely and relevant information on-site is an important task for mobile users. A context-aware system can understand a user's information needs and thus select contents according to relevance. We propose a context-dependent search engine that represents user context in a knowledge-based context model, implemented in a hierarchical structure with granularity information. Search results are ordered based on semantic relevance computed as similarity between the current context and tags of search results. Compared against baseline algorithms, the proposed approach enhances precision by 22% and pooled recall by 17%. The use of size-based granularity to compute similarity makes the approach more robust against changes in the context model in comparison to graph-based methods, facilitating import of existing knowledge repositories and end-user defined vocabularies (folksonomies). The reasoning engine being light-weight, privacy protection is ensured, as all user information is processed locally on the user's phone without requiring communication with an external server.