Recurrent neural network method for improving time sequence data mining capability of recommendation model

Abstract

The invention discloses a recurrent neural network method for improving the time sequence data mining capability of a recommendation model. The method includes the following steps that: step 1, time gate-based network transformation is performed on a conventional neural network; step 2, interaction interval time-added time sequence data are inputted into a model; step 3, the prediction values of the model on each item of the sequence are calculated; step 4, the loss value of the model is calculated, if the loss value is lower than a preset value and tends to be stable, step 6 is executed, otherwise, step 5 is executed; step 5, the gradient of each parameter is calculated according to the loss value, the parameters are updated, the method returns to the step 3; and step 6, the interest of auser is predicted based on the current model. With the method of the invention adopted, the time information mining capacity of the neural network in the recommendation field can be improved, and themodel can process long-term general features and short-term temporary features contained in long-term data more easily, and can perform even better in a personalized recommendation system.

Description

technical field [0001] The invention relates to the field of machine learning and personalized recommendation, in particular to a cyclic neural network method for improving the time series data mining ability of a recommendation model. Background technique [0002] The rapid development of the information society has accumulated a large amount of data, and the historical data generated by users on the Internet reflects their own interests and hobbies. These data provide Internet vendors with the possibility to predict user interests and make targeted recommendations. The recommendation system is an effective way for Internet manufacturers to recommend products to users, and its accuracy directly affects the return rate of manufacturers' advertising investment and the user's consumption experience. [0003] At present, the recommendation system has been widely used in the fields of commodities, music, news and so on. The recommendation model mostly uses the user's purchase ...

AI Technical Summary

Problems solved by technology

The Long Short Term Memory (LSTM) model is one of the models that utilize temporal information in neural networks, but it also fails to directly utilize the specific time information of each item in the sequence data.

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