MOOC forum post recommendation method integrating forum interaction behaviors and user reading preferences

Abstract

The invention discloses a MOOC forum post recommendation method integrating forum interaction behaviors and user reading preferences. The MOOC forum post recommendation method comprises the followingsteps: 1) constructing a target scoring matrix, a user interaction scoring matrix and a user interaction frequency matrix of a user to posts; 2) decomposing the user interaction score matrix to obtaina user objective function and a user behavior feature matrix; 3) calculating the number of times of user interaction behaviors, extracting embedded features, and obtaining a user matrix based on theuser behavior feature matrix and the user embedded matrix; 4) extracting post topics through a noise reduction auto-encoder to obtain an object target function, and decomposing the target score matrixto obtain a score matrix target function; and 5) optimizing the scoring matrix objective function, the user objective function and the article objective function, and providing a recommendation listfor the user. Through deep learning and probability-based matrix decomposition, subtle interaction between the users and between the posts and between the users is fused into the model, the cold startproblem is relieved, and accurate recommendation is realized in combination with user reading preferences.

Description

technical field [0001] The invention relates to the technical field of teaching and education data mining and natural language processing, in particular to a method for recommending MOOC forum posts that integrates forum interaction behavior and user reading preferences. Background technique [0002] In the era of high-speed dissemination of network information and rapid development of computer technology, the online learning platform enables a new generation of netizens to learn courses from famous schools anytime, anywhere. Massive Open Online Course (MOOC for short) has attracted thousands of scholars. By the beginning of 2018, the number of students studying in my country exceeded 70 million. Although there are a large number of students, due to the openness of the MOOC itself, the level of the students is uneven, and the learning goals are different, resulting in a high dropout rate and low participation in the MOOC. As an important module to promote students' knowled...

AI Technical Summary

Problems solved by technology

The existing MOOC forum post recommendation algorithm uses more traditional methods such as LDA topic model, word co-occurrence statistics, and associated words to extract user reading preferences. However, traditional methods such as topic models are difficult to combine with user behavior information for end-to-end recommendation, and the recommendation is accurate. degree is not high

On the other hand, the existing technology lacks consideration of scenarios where MOOC forum posts recommend users with low activity, and no solution is proposed for the cold start problem.

[0004] The present invention proposes a recommendation method that integrates forum interaction behaviors and user reading preferences. Deep learning and probability-based matrix decomposition integrate the subtle interaction information between users and posts and between users and users into the model to more completely describe user preferences, alleviate cold start problems, and achieve accurate recommendations

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