Recommendation ranking method based on wide-depth gate-cycle joint model

Abstract

The invention relates to a recommendation sorting method based on a wide-depth gate-cycle joint model, and belongs to the technical field of natural language processing. The present invention first crawls the Sina microblog data for preprocessing to obtain the theme feature set; secondly, uses generalized cross feature transformation to memorize the theme features, and inputs them into the linear module; then, learns embedding vectors for each classification feature, and integrates The embedding vector of is concatenated with the dense features, and the dense vector produced by the concatenation is input to a deep module consisting of gated recurrent units. Finally, the parameters in the linear and deep loop processes are optimized simultaneously, and the recommendation ranking results are obtained through joint training of the models. The present invention uses gated recurrent units to perform feature generalization, which improves the problem that most of the previous methods do not consider the sequence features of dynamic time series, achieves better recommendation effects on the whole, and improves recommendation efficiency to a certain extent.

Description

technical field [0001] The invention relates to a recommendation sorting method based on a wide-depth gate-cycle joint model, and belongs to the technical field of natural language processing. Background technique [0002] In recent years, with the prevalence of online social network systems, Weibo provides a very open communication channel for people to read, comment, quote, socialize, which includes text-based Weibo entries and configuration files, pictures, data and Multimedia and many other valuable resources. The rapid development of personalized recommendation service in Weibo social network combined with other product areas has undergone a fundamental paradigm shift. In the face of massive amounts of information, how to quickly locate user characteristics, how to effectively recommend resources they are interested in to users, and how to explore features that have never been or rarely found in the past based on historical data, and use deep learning technology to imp...

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