| CPC G06Q 30/0631 (2013.01) [G06F 16/9535 (2019.01); G06N 3/08 (2013.01); G06Q 30/0641 (2013.01)] | 28 Claims |
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1. A system for analyzing online temporal interactions of a user, the system comprising a user computational device for operation by the user, a server for analyzing said online temporal interactions of the user and a computer network for connecting the user computational device to the server, wherein said user computational device comprises a user interface for receiving user actions and for displaying content to the user, said server comprises a recommendation engine for analyzing said user actions and for recommending additional content for provision to said user interface; wherein said recommendation engine comprises a plurality of models, comprising a first model for analyzing a current online behavior of the user arranged in a session, and a second model for analyzing past online behavior of the user, wherein said recommendation engine concatenates outputs of both models to a single output, to determine additional content for display through said user interface; wherein said server comprises a memory for storing a plurality of instructions for operating said recommendation engine and a processor for executing said plurality of instructions; wherein said first model comprises a CNN (Convolution Neural Network), wherein said CNN comprises a plurality of overlapping filters having different filter shapes and wherein said CNN has a single 3D convolution layer only, such that said plurality of overlapping filters having different filter shapes are all present in said single layer; wherein said CNN receives each character in a time sequence during said session; wherein said output of said single 3D convolution layer is fed to a 3D max pooling layer, wherein said second model comprises a transformer-based model, wherein said transformer-based model comprises an encoder alone, without a decoder; wherein said transformer-based model receives a plurality of tokens corresponding to said past online behavior of the user; wherein said recommendation engine concatenates outputs of both models to a single output through a concatenation layer;
further comprising training said CNN by receiving data comprising data obtained from current online behavior of the user comprising actions taken by the user in a current session;
preprocessing said data to remove noise; feeding said data to said CNN character by character; then applying an Adam optimizer and L2 regularization, after first initializing weights for said CNN with a truncated Gaussian;
further comprising training said transformer-based model by receiving data comprising data obtained from past online behavior of the user, comprising actions taken by the user in a previous session; preprocessing said data to remove noise; tokenizing said data and feeding said tokenized data to said transformer-based model.
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