SAPRS: Situation-Aware Proactive Recommender system with explanations

Proactive recommender systems are smart applications which deliver the recommendations on users' mobile devices automatically, without their intervention. Such systems help the users in timely reception of the information of their interest. Improving user's acceptance on pushed recommendations of these systems is a challenging task. In these systems, determining right push context (situation) and finding relevant items for the target user are considered as two vital issues for achieving better user acceptance. Moreover, along with the pushed recommendations, if the target user is also shown the explanation why something is recommended to him then this transparency might help the user to make a better decision & increase his faith in the pushed recommendations for improving user's acceptance. Therefore, we present a Situation-Aware Proactive Recommender System (SAPRS) that pushes both relevant and justifiable recommendations to the target user at the right context only in order to achieve better user acceptance. SAPRS works in two phases; (i) situation assessment phase and the (ii) item assessment phase. In situation assessment phase, the proposed system analyzes the current situation i.e. whether or not the current context needs a recommendation to be pushed. In the Item assessment phase, SAPRS generates relevant recommendations for the target user using a location-aware reputation based collaborative filtering algorithm. It also enhances the transparency of the pushed recommendations by means of explanations in this phase. The prototype of SAPRS is implemented using multi-agent approach for restaurant recommendations and its performance is evaluated using precision, recall metrics and feature based comparisons.