A Rapid Scalable Embedding and Mapping Model for Recommendation

The recommendation systems based on deep neural networks show high accuracy and precision in advertising prediction and rating prediction tasks, but the performance declines when they encounter sparsity problems. Furthermore, the low scalability framework makes them expand new users or items inefficiently. We propose a Rapid Scalable Embedding and Mapping model for Recommendation (RSEMR) to solve the problems. RSEMR uses the bi-directional mapping structure to build the transformation between the embedding vector and the unified vector space, which can rapidly expand new users or items into the existing domains and achieve a better initial performance of prediction. Two effective mapping structures, including vector elements-based single connection and autoencoder-based fully connection, can be used in RSEMR to capture linear or non-linear mapping functions, respectively. Besides, RSEMR does not require any additional information other than a very small amount of user behavior data, and it deals with the relationships between subjects based on the spatial distance in the unified vector space, which is more adaptable to the scarce attribute labels and sparse interaction behaviors. We select several open datasets for experiments and the results have demonstrated RSEMR's superiority over the mainstream neural network-based recommendation models in sparsity situation, in terms of accuracy, efficiency, and scalability.