Abstract
A Proactive Recommender System (PRS) actively pushes recommendations to users when the current context seems appropriate. Despite the advantages of PRSs, especially in the mobile scenario where users could be provided with relevant items on-the-fly when needed, the area of PRSs is still unexplored with many challenges. In particular, it is crucial to identify the relevant items for the target users as well as to determine the right context for pushing these items, since otherwise the user acceptance, and therefore system success, will be negatively impacted. In this paper, we propose a new model that scores each item on two dimensions, preference fit and context fit, to proactively push relevant items to the target user in the right context. Furthermore, we present the preliminary design of a prototype of a mobile Point of Interest (POI) recommender which will be implemented in order to evaluate the practicality and effectiveness of our proposed model.