Abstract: Techniques are described herein that are capable of performing transfer-learning for structured data with regard to journeys defined by sets of actions. A first deep neural network (DNN) for a first journey is trained using structured data. Weights of nodes in the first DNN are transferred to nodes in a second DNN for a second journey using transfer-learning. An embedding layer replaces a final layer of the first DNN in the second DNN to provide an output with a same number of nodes as a pre-final layer of the first DNN. Weights of the nodes in the embedding layer are initialized based at least on a probability that a new feature of the second journey co-occurs with each feature in the structured data. A softmax function is applied on a final layer of the second DNN to indicate possible next actions of the second journey.