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Neural network-based collaborative filtering systems focus on designingnetwork architectures to learn better representations while fixing the input tothe user/item interaction vectors and/or ID. In this paper, we first show thatthe non-zero elements of the inputs are learnable parameters that determine theweights in combining the user/item embeddings, and fixing them limits the powerof the models in learning the representations. Then, we propose to learn thevalue of the non-zero elements of the inputs jointly with the neural networkparameters. We analyze the model complexity and the empirical risk of ourapproach and prove that learning the input leads to a better generalizationbound. Our experiments on several real-world datasets show that our methodoutperforms the state-of-the-art methods, even using shallow network structureswith a smaller number of layers and parameters.

Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering