The learning similarity between users and points of interests (POIs) is an important function in location-based social networks (LBSN), which could primarily benefit multiple location-based services, especially in terms of POI recommendation. As one of the well-known recommender technologies, Collaborative Filtering (CF) has been employed to a great extent in literature, due to its simplicity and interpretability. However, it is facing a great challenge in generating accurate similarities between users or items, because of data sparsity. Traditional similarity measures which rely on explicit user feedback (e.g., rating) are not applicable for implicit feedback (e.g., check-ins). In this study, we propose multi-criteria user–user and POI–POI similarity measures, based on the dominance concept. In this regard, we incorporate geographical, temporal, social, preferential and textual criteria into the similarity measures of CF. We are interested in measuring POI similarity from a location perspective, by taking into account the influence of the dominance concept on multiple dimensions of POIs. To evaluate the effectiveness of our method, a series of experiments are conducted with a large-scale real dataset, collected from the Foursquare of two cities in terms of POI recommendation. Experimental results revealed that the proposed method significantly outperforms the existing state-of-the-art alternatives. A further experiment demonstrates the superiority of the proposed method in alleviating sparsity and handling the cold-start problem affecting POI recommendation.
