Information recommendation method based on convolutional neural network and joint attention mechanism

Abstract

The invention relates to an information recommendation method based on a convolutional neural network and a joint attention mechanism, which is used for effectively utilizing potential semantic information of text and overcoming inherent defects of a feature extraction method of traditional machine learning. According to the method, feature vectors of the evaluation text processed by a CNN deep neural network is processed by a layer of attention mechanism, so that the attention weight of key points of interest in the evaluation text is increased. The vector sets of users and projects and thescore of the previous attention mechanism respectively use a layer of attention mechanism to acquire attention mechanism weight vectors of the users and the projects respectively. Point multiplication is carried out on the attention mechanism weight vectors and vector sets of the users and the projects respectively to obtain final representation, the users, the projects and the evaluation text are combined to obtain the final representation, and score prediction is made. Compared with traditional recommendation technology, the method has the advantages that recommendation can be performed more effectively, the recommendation quality is improved, and the interpretability of recommendation is enhanced.

Description

[0001] 1. Technical field [0002] The invention relates to a text feature extraction and joint attention mechanism recommendation method based on deep learning, and belongs to the field of information recommendation. [0003] 2. Background technology [0004] The main task of information recommendation is to recommend according to the user's behavior records, find the most suitable items and recommend them to users, analyze the user's historical behavior, model the user's interest, and actively recommend to the user that can meet the user's interest and needs. information. The recommendation system is an interdisciplinary subject, including machine learning (Machine Learning), data mining (Data Mining), information retrieval (Information Retrieval) and human-computer interaction (Human-Computer Interaction) and other fields. [0005] Deep learning technology has made major breakthroughs in the fields of image processing, natural language processing, and speech recognition, an...

AI Technical Summary

Problems solved by technology

[0007] None of the above-mentioned algorithms have effectively utilized the potential semantic information of the text, and the traditional machine learning feature extraction method has some inherent defects.

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