A Personalized Academic Literature Recommendation Method

Abstract

A personalized academic literature recommendation method, including the following steps: S1 data collection and cleaning: collecting paper data with papers and authors as the core, the paper data including paper titles, paper abstracts, author names, publication years, publications and references, cleaning out data with obvious format errors and missing data; S2 model establishment, the process is as follows: S2.1 Construction of training set; S2.2 Feature calculation; S3 model training; S4 Academic literature recommendation, the process is as follows: S4 .1 To establish a set of candidate documents, it is required that the publication time of the cited paper selected in each step is earlier than the publication time of the paper; S4.2 Forecasting, the paper with the highest k′ in the probability value is taken as the final recommended reference. The invention can more accurately and efficiently generate a list of references meeting user needs.

Description

technical field [0001] The invention relates to the field of machine learning and data mining, and further provides a method for recommending reference documents considering user preference. Background technique [0002] Finding relevant and important references is an important way for researchers to understand the most cutting-edge research results in their field, and obtain the latest research trends and development directions. [0003] Today, researchers still manually select papers that may be related to their current research field by giving topics, keywords, etc. in a search engine such as Google Scholar or a specific database such as Web of Knowledge. The rapid growth of the number of academic literature published in recent years, coupled with the popularity of electronic publications and open databases, on the one hand, highlights the shortcomings of the current manual selection method, such as time-consuming, low accuracy, and mechanization of the operation process....

AI Technical Summary

Problems solved by technology

The rapid growth of the number of published academic documents in recent years, coupled with the popularity of electronic publications and open databases, on the one hand, highlights the shortcomings of the current manual selection method, such as time-consuming, low accuracy, and mechanized operation. On the one hand, the existence of a large amount of academic data also makes it possible to use various data-driven methods such as data mining to automatically generate reference lists

[0004] Existing literature retrieval and recommendation methods are often not fully functional and cannot produce satisfactory personalized recommendation effects. At the same time, there is also the problem of cold start, which cannot provide effective recommendations for users who lack sufficient information.

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