Meta-Heuristic Approach for Recommending Routes in Map Using Hybrid Recommender System

Gurbinder Kaur, Dr. Manmohan Sharma

Abstract: This paper demonstrates that ant colony optimization can improve map routing efficiently. This paper presents a recommender system that provides personalized information about locations of potential interest to a tourist. The system generates suggestions, consisting of touristic places, according to the current position and history data describing the tourist movements. For the selection of tourist sites, the system uses a set of points of interest a priori identified. A Dynamic Travel Path Optimization System based on Ant Colony Optimization (ACO) is proposed for the prediction of the best path to a given destination. The concept relies on the hybrid recommender system for map routing which can be used as the alternative to the current systems. This system implements hybridization of content and collaborative-filtering. This system also optimized the retrieved results by using proposed heuristic approach. The proposed system has also taken care of the cold start problem for new users. Simple GUI is developed to manage all these approaches.

Keywords: Recommender system, collaborative tagging, content filtering, heuristic approach, cold start problem