Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.

Ride-sharing, which refers to assigning a set of riders for saving travel miles and alleviating traffic pressure, has drawn increasing attention. Existing works emphasize compatibility of potential riders on the basis of geographic proximity. They generally assume that no rejection would happen after the assignment is completed by the server. However, ignorance of psychological factors on ridesharing (e.g., trust on car mates) can lead to decrease rider acceptance. Thus, in this paper, we take the tendency of a rider to group with others into consideration and maximize riders' acceptance when sharing a trip. Specifically, we formally define the problem of maximizing riders' acceptance based on people′s interests, social links, and employ social networking to facilitate finding a ridesharing group for the rider with the largest acceptance. We propose a new ride-sharing mode to recommend groups that travel together from geo-social data streams. To optimize the recommendation, we develop a heterogenous travel network, based on a proposed destination-prediction algorithm, to mine the similar spatial movements among a set of riders. Then, we measure the willingness of riders for joining in a group using social context. Finally, we progressively select the riders with high acceptance to be in the top-k results. We present the results of applying framework on real world social media data from the Twitter. Computational results show our method is able to significantly reduce the travel time when ridesharing, while keeping a high level of acceptance on real-world datasets.