Point-of-interest 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. 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. (shrink)

Transgender women are an important subgroup of the transgender umbrella and have their own unique gender identity. This article aimed to understand and measure the latent concept of gender identity among Chinese transgender women from a multi-dimensional perspective. Through a two-phase, iterative scale development process, we developed the Gender Identity Scale for Transgender Women in Chinese. Literature reviews, expert consultations, and focus groups constitute phrase 1 of the study, which resulted in the first version of GIS-TW with 30 items. In (...) phrase 2, exploratory factor analysis on a sample of 244 Chinese transgender women revealed a six-factor solution across the 22 items. The Bem Sex Role Inventory was included to test for convergent validity, and the Rosenberg Self-Esteem Scale was used to test discriminant validity. Then we conducted the confirmatory factor analysis with an independent sample of 420 Chinese transgender women, which produced the final version of GIS-TW with 21 items. The internal consistency and test-retest stability of each factor was good. In conclusion, the GIS-TW is a reliable and valid psychometric tool for the assessment of Chinese transgender women’s gender identity. Future application of the scale will help transgender women obtain better gender confirmative interventions. (shrink)