Time sequence recommendation algorithm based on generation sorting

Abstract

The invention discloses a time sequence recommendation algorithm based on generation sorting, and belongs to the technical field of recommendation systems. The method comprises the specific steps of 1, randomly sampling a data sample, transmitting the data sample to a recommendation model, and scoring the data sample through the recommendation model; 2, converting the data into corresponding recommendation scores through one-hot coding, an embedding layer, a generation layer and a conversion layer; 3, updating recommendation model parameters; 4, judging whether the accuracy of the recommendation model is improved or not; and 5, after the training of the recommendation model is finished, scoring by using the recommendation model, sorting according to a scoring result, and recommending the project to the user. Aiming at the problem that data sparsity and user preferences change along with time due to a large number of users and products in recommendation system training data, negative sampling and a Gaussian distribution-based generation method are used for training the model, so that the time sequence model has generalization ability and can identify changes of user preferences; finally, the accuracy of recommendation is improved.

Description

technical field [0001] The invention relates to the field of recommendation algorithms, in particular to a sequence recommendation algorithm based on generation sorting. Background technique [0002] Recommendation algorithms are widely used in different types of websites, such as video, shopping, news, and learning, and have received extensive attention from industry and academia. The recommendation system can effectively use the user's historical interaction information to help users filter out uninteresting content and quickly obtain useful information from massive network data. The recommendation system uses recommendation algorithms to generate a list of content that users may be interested in, and can recommend high-quality and latest content to users to keep users active online. [0003] Traditional recommendation algorithms such as collaborative filtering and content-based filtering recommendation algorithms can use users' historical data to generate perceptual reco...

AI Technical Summary

Problems solved by technology

[0004] In the process of training the traditional recommendation algorithm model, the model projects high-dimensional data into a low-dimensional feature space, and finally outputs the corresponding scores for sorting. However, when the training data is very sparse, the model cannot have good generalization ability

And when the user chooses a product of a different type from the history, the model cannot change the recommendation list, which eventually leads to a lower recommendation accuracy

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