Techniques for Improving Performance of Recommender Systems for Tourist Point of Interest Recommendation

Among the various applications of recommender systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identify user interests is to use collaborative filtering (CF) technique. However, the accuracy and efficiency of CF can be improved by applying different parameters and complementary approaches. In this paper, a new solution for promoting POI offers to tourists is presented, which uses a five-dimensional time model including the dimensions of day and night hours, days of the week, days of the month, months of the year, and occasions, and by calculating the Euclidean distance between the time of recommendation and the time of previous experiences of the active user and his similar users identifies and suggests suitable venues. The proposed solution also uses the trust parameter to increase the accuracy of POI suggestion. To improve the accuracy of trust evaluation, a new criterion based on a similarity tree structure between contexts is introduced. The results of experiments conducted on three well-known datasets show that the proposed model outperforms the state-of-the-art methods in term of efficiency and accuracy.