A Study on the Method of Mobile Content Recommendation Based on Situations

In existing mobile content service systems, the study is quite rare on automatic situation-service rule construction. Hence, a method is proposed that the semantic association rules between situations and preferences are built by quantitative frequent marked lattice. Different recommendation rules can be extracted along multi-dimensional context routes from this lattice structure. It is propitious to solve the problems of rule collisions and availability of context data. The algorithm is studied for frequent marked lattice construction and the priority is designed for rule extraction. Finally, the advantage and feasibility of the proposed method is demonstrated by a calculating instance.