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Recommendation sorting method based on wide and deep gate loop joint model

A joint model and sorting method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problem of not considering the improvement of item diversity and time series changes at the same time, so as to improve the recommendation efficiency and make good recommendations effect, good effect

Active Publication Date: 2018-10-12
KUNMING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

However, these methods do not consider both the diversity of promotion items and the variation of time series

Method used

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  • Recommendation sorting method based on wide and deep gate loop joint model
  • Recommendation sorting method based on wide and deep gate loop joint model
  • Recommendation sorting method based on wide and deep gate loop joint model

Examples

Experimental program
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Embodiment 1

[0050] Embodiment 1: as Figure 1-4 As shown, the recommended sorting method based on the wide-depth gate-cycle joint model, the specific steps of the method are as follows:

[0051] Step1. First crawl the microblog blog post data, arrange the data samples proportionally through manual annotation, and obtain the training set, verification set and test set corpus, and then use the topic extraction method based on LDA and sparse autoencoder to extract Extract topics from blog posts and obtain topic feature sets;

[0052] Step2. Construct the linear module of the wide-depth gate cycle model, classify according to the topic features in Step1, use cross feature conversion to memorize features, and use logistic regression to predict the possibility of establishing a relationship between user features and candidate blog posts, where the input Including the original features of user attributes and the intersection features of historical click data sets;

[0053] Step3. Construct the...

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Abstract

The invention relates to a recommendation sorting method based on a wide and deep gate loop joint model, and belongs to the technical field of natural language processing. According to the method, Sina Weibo data are first crawled for preprocessing to obtain a topic feature set; then generalized cross-feature conversion is used to memorize topic features, and the same is input into a linear module; then an embedding vector is learned for each classification feature, all the embedding vectors are connected with dense features, dense vectors generated by connection are input to a deep module formed by gate loop units; and finally, parameters in linear and deep loop processes are simultaneously optimized, and a recommendation sorting result is obtained through joint training on the model. According to the method, the gate loop units are used for feature generalization, the problem that conventional methods mostly do not consider sequence features of dynamic time sequences is alleviated, abetter recommendation result is achieved as a whole, and recommendation efficiency is also improved 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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 黄青松王艺平李帅斌郎冬冬赵晓乐谢先章
Owner KUNMING UNIV OF SCI & TECH
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