Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These techniques have proven successful in stationary regimes, but the transition to an interactive and social Web, and the rise of user-generated content, increasingly require learning from dynamic factors. Existing approaches, based on distributed vector representations, notoriously fail in fast-changing contexts and sparse regimes; their static representation of users and items prevents them from adapting to contextual changes. Given this limitation, this thesis focuses on introducing new methods to make predictions based on contextual variables, exploit rich (social, temporal) signals, and maintain model consistency over time. First, we study a recommendation task on a live-streaming platform with a dynamically evolving set of available items. In this context, users have to choose from a limited subset of creators that are currently streaming content. To model this setting, we introduce a self-attentive approach that draws a dependency between available options and re-ranks the most promising candidates. We also show that repeat consumption, and the time at which it occurs, are predictive factors that we incorporate into our model. Second, we propose a dynamic embedding scheme to maintain a latent space consistent over multiple temporal slices of a dataset, by penalizing unnecessary differences between successive solutions of the model. In order to highlight the potential for analysis of our approach, we apply our methodology to a dataset of news production. Thanks to its ability to propagate information over several time epochs, our model sheds light on important changes in news coverage induced by acquisitions of media companies. Third, we propose an approach for modeling reciprocal interest on a bartering platform where users exchange goods, for which we design a trade recommendation system. We find that the social ties between members have a strong influence, as does the time at which they trade, therefore we extend our model to be socially- and temporally-aware. Finally, we study the collaboration between users on Reddit Place, a social experiment where users drew together on a virtual canvas. We propose to use personalization methods to model user actions, capture the latent structure of this emergent collaborative effort and provide an interpretable representation of its social structure.