A Personalized Recommendation Method Based on Deep Learning

Abstract

The invention discloses a personalized recommendation method based on deep learning. The method comprises the steps of according to the viewing time sequence behavior sequence of the user, predictingthe next movie that the user will watch, including three stages of preprocessing the historical behavior characteristic data of the user watching the movie, modeling a personalized recommendation model, and performing model training and testing by using the user time sequence behavior characteristic sequence; at the historical behavior characteristic data preprocessing stage when the user watchesthe movie, using the implicit feedback of interaction between the user and the movie to sort the interaction data of each user and the movie according to the timestamp, and obtaining a corresponding movie watching time sequence; and then encoding and representing the movie data,wherein the personalized recommendation model modeling comprises the embedded layer design, the one-dimensional convolutional network layer design, a self-attention mechanism, a classification output layer and the loss function design. According to the method, the one-dimensional convolutional neural network technologyand the self-attention mechanism are combined, so that the training efficiency is higher, and the number of parameters is relatively small.

Description

technical field [0001] The present invention relates to the technical field of recommendation systems, in particular to a personalized recommendation method based on deep learning. Background technique [0002] The recommendation system is a connector between people and information, using existing user characteristics and past user interactions to predict possible future interactions between users and information content. The recommendation system selects the recommendation algorithm according to the historical behavior of different users, the user's interest preference or the user's demographic characteristics, or establishes a recommendation model, uses the recommendation algorithm or model to generate a list of items that the user may be interested in, and finally pushes it to user. [0003] In recent years, with the continuous development of deep learning research, a large number of recommendation algorithm models based on deep learning have been proposed. Recommendati...

AI Technical Summary

Problems solved by technology

Causal convolution means that the output of the t-th time step in the time series can only depend on the input of the previous t steps. In order to prevent information leakage, future information cannot be used

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