Recommender systems have been deployed in many diverse settings, and they aim to provide a personalized ranked list of items to users that they are likely to interact with. In order to provide an accurate list of items, models need to capture various aspects of the users' profiles, behaviors, and items' dynamics. Depending on the recommendation settings, these aspects can be mined from the different auxiliary information sources that might be readily available in these settings as side information. The more aspects being covered, the more accurate the learned user and item representations will be, improving prediction performance and overcoming various challenges such as sparse interaction data. These auxiliary information sources might contain static attributes related to the users' and items' profiles or contain historical multi-relational implicit interactions between users and items, users and users, and items and items such as clicks, views, bought-together, and friendships. These interactions can be exploited to capture complex implicit relations that are usually not visible if the model only focuses on one user-item relationship. Besides attributes and interaction data, auxiliary information might also contain contextual information that accompanies the interaction data, such as timestamps and locations. Incorporating such contextual information allows the models to comprehend the dynamics of users and items and learn the influence of time and environment. In this thesis, we present four ways in which auxiliary information can be leveraged to improve the prediction performance of recommender systems and allow them to overcome many challenges. Firstly we introduce an attribute-ware co-embedding model that can leverage user and item attributes along with a set of graph-based features for rating prediction. In particular, the model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the co-occurrence graph. We also demonstrate that our ...