© 2020 Jiayuan He ; The rapidly growing location-based social networks allow web users to check-in at point- of-interests (POIs) and share their check-ins with the public. The large amount of web breadcrumbs left by users has enabled researchers to investigate human mobility patterns, which opens new research opportunities for incorporating better personalization into location-based services. In general, two types of recommendation tasks have been extensively investigated. The first type is POI recommendation which aims to provide a ranking list of POIs according to their attractiveness to a user. The second type is trip recommendation which aims to suggest an itinerary, i.e., an ordered sequence of POIs, for users. Developing recommendation models for POIs is challenging mainly due to three reasons. First, the observed POI visits of an individual user are limited in quantity. Second, users’ preferences over POIs are usually influenced by various contextual factors. Examples of these contextual factors include sequential contexts (i.e., the influence of a user’s recent POI visits on her next visit), temporal contexts (i.e., the influence of the visiting time on a user’s preference over POIs), and geographical contexts (i.e., the influence of a user’s geographical location on her preference over POIs). Finally, another reason that makes the development of POI recommendation models challenging is that the valuable information (e.g., the temporal contexts, the reviews left by users, and the geo-tagged photos post by users), which is useful to enhance the recommendation accuracy, has heterogeneous forms (e.g., the numerical, textual, and visual forms). In this thesis, we aim to address the following questions in the domain of POI recommendations: 1. How can we capture the complex interactions between users’ preferences and the contextual factors effectively and efficiently? 2. How can we model the temporal dynamics in users’ preferences over POIs given the limited observations of users’ historical POI visits? 3. How ...