Successive location recommendation has recently emerged as an important service in Location-Based Social Networks (LBSNs). It aims at recommending the next location(s) to visit to a user given its current and previous locations. Although several recommenders have been proposed, only few works have considered the sequential correlations among locations in addition to other influential factors in recommendation. In this paper, a novel sequential rule mining-based approach called Location Recommender (LocRec) is proposed. Our proposal is designed to perform successive location recommendation for users of LBSN by considering sequential, social and temporal influence factors. The proposed approach first extracts the set of location sequences from mobility data and then generates recommendation rules from it. Based on the concept of recommendation influence factor, two rule-based recommenders are designed, namely temporal-based and social-based recommenders. An experimental evaluation was conducted on a real large-scale LBSN dataset to compare the performance of the proposed recommenders. Obtained results show that the tolerance to order-variations and the use of a window constraint enhance the performance of LocRec. They also depict that LocRec outperforms classical sequential-based models for successive location recommendation.