A Scholars Recommendation and Cooperation Prediction Method Based on Representation Learning and Competition Theory

Abstract

The invention provides a scholar recommendation and cooperative prediction method based on representation learning and competition theory, belonging to the field of computer software. With the huge academic network, the recommendation needs are met, through the presentation learning technology, the paper published by the author and the network relationship among scholars are analyzed, with the help of competition theory, the time conflict is solved in order to obtain the recommendation results and collaborative forecasting. The method obtains valid data from Microsoft dataset and preprocessesthem, constructs a dynamic model for calculating the similarity of scholars' personality based on the content of the paper, constructs a collaborative network-based model for computing the similarityof academic environments, and constructs the processing model of competition theory, the model is trained with the preprocessed data set, according to the personality similarity obtained from the training, a preliminary recommendation list is generated to weaken the source and target scholars who are too similar by using the environmental similarity, and the time conflict is eliminated by using the processing model of competition theory, so as to achieve effective scholar recommendation and the next collaborative object prediction.

Description

technical field [0001] The invention belongs to the field of computer software, and relates to a scholar recommendation and cooperation prediction method based on representation learning and competition theory. Background technique [0002] Interdisciplinary research across multiple fields has grown rapidly over the past few decades, and scientific collaboration among scholars has become increasingly important and necessary. However, finding the most valuable collaborators from large academic data is often a great challenge. The purpose of representation learning is to assign a vector in a certain linear space to each node in the network, which is what we often call an N-dimensional vector. The relationship between vectors and vectors retains the relationship between the corresponding nodes in the original network. structural relationship. Word2vec was released by Google in 2013. It integrates the CBOW and Skip-Gram models and provides an efficient implementation for compu...

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Problems solved by technology

[0006] The present invention provides a scholar recommendation and cooperation prediction method based on representation learning and competition theory, which solves the problem of poor effect caused by traditional static recommendation and single-source cooperation prediction, realizes dynamic cooperation recommendation and cooperation prediction, and ensures the accuracy of results accuracy

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