Bartering is a timeless practice that is becoming increasingly popular on the Web. Recommending trades for an online bartering platform shares many similarities with traditional approaches to recommendation, in particular the need to model the preferences of users and the properties of the items they consume. However, there are several aspects that make bartering problems interesting and challenging, specifically the fact that users are both suppliers and consumers, and that the trading environment is highly dynamic. Thus, a successful model of bartering requires us to understand not just users’ preferences, but also the social dynamics of who trades with whom, and the temporal dynamics of when trades occur. We propose new models for bartering-based recommendation, for which we introduce three novel datasets from online bartering platforms. Surprisingly, we find that existing methods (based on matching algorithms) perform poorly on real-world platforms, as they rely on idealized assumptions that are not supported by real bartering data. We develop approaches based on Matrix Factorization in order to model the reciprocal interest between users and each other’s items. We also 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. We evaluate our approach on trades covering books, video games, and beers, where we obtain promising empirical performance compared to existing techniques.