Hierarchical Attention deep learning model course recommendation method

Abstract

The invention relates to the technical field of machine learning, deep learning, data mining and the like, in particular to a hierarchical Attention deep learning model course recommendation method. According to the method, dynamic changes of user interests are modeled by utilizing user sequential behavior data and using LSTM, and long-term and short-term preferences of a user are obtained by constructing a hierarchical Attention structure, so that advanced mixed representation of the user is generated, and user personalization and accuracy of a recommendation result are improved. The method specifically comprises the following steps: performing screening and preprocessing by using original online learning related behavior data, dividing the sequential behavior of a user into sessions, and then processing three types of data of fine granularity (information user ID and course ID) and coarse granularity information (course type) by using an embedding layer and a full connection layer to obtain user vector representation; capturing interaction and evolution of different historical session interests of a user by applying LSTM (Long Short Term Memory) to obtain a serialized interest vector, and inputting the interest vector into an Attention network to obtain long-term interest representation of the user; inputting the recent behavior data and the long-term interest representation of the user into a second-layer Attention network to obtain a mixed interest representation of the user; and finally, carrying out inner product on the mixed interest representation and the course vector representation of the user, taking an obtained value as a score of the candidate item, and sorting the scores of the candidate item to obtain a recommendation list for carrying out personalized recommendation on the student.

Description

(1) Technical field [0001] The invention relates to technical fields such as machine learning, deep learning, and data mining, and in particular to a method for recommending courses for a layered Attention deep learning model. (2) Background technology [0002] In recent years, with the rapid development of cloud computing, big data, artificial intelligence and other technologies, great changes have taken place in the field of education. Well known as Massive Open Online Courses (MOOCs for short), it is an open course that aims to provide educational content to a large number of participants through an online platform and provides free access. The term MOOC was invented in 2008 and has grown in popularity since 2012, creating a new model of education. Top U.S. universities and their professors have established several MOOC platforms, such as Udacity, Course, and edX, and become leaders in this field. In recent years, a large number of MOOC platforms have emerged in China, ...

AI Technical Summary

Problems solved by technology

[0004] What the present invention aims to solve is that in current course-based recommendation methods, only a static and low-rank vector is used to simulate user interest, thereby ignoring the problem that user interest is dynamically changing; at the same time, in the course feature construction process, no The course features and knowledge points contained in the course text information lead to problems such as the failure of the generated recommendation model to recommend personalized courses for users. A method for course recommendation based on a hierarchical Attention deep learning model is provided.

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