Scientific and technical literature recommendation method based on author frequent pattern

Abstract

The invention discloses a scientific and technical literature recommendation method based on an author frequent pattern. The scientific and technical literature recommendation method comprises the following steps: adding a function of processing literature ID (Identity) (the unique number of the literature) into a Fp-growth algorithm, constructing the author frequent pattern, a literature set corresponding to the author frequent pattern, literature published by the author and a literature set corresponding to a keyword, and removing the literature browsed by a user to construct a literature recommendation candidate set. The invention also constructs a scoring system which sorts the candidate sets according to the descending order of the scores to recommend ten literatures with the highest score to the user.

Description

technical field [0001] The invention belongs to the field of recommendation systems, and relates to a method for recommending scientific and technological documents based on author's frequent patterns. Background technique [0002] The emergence and popularization of the Internet has brought a lot of information to users, but with the rapid development of the network, the amount of information on the Internet has increased significantly, making it impossible for users to obtain the part that is really useful to them when faced with a large amount of information. Information, the efficiency of using information is reduced instead, which is the so-called information overload problem. A very potential solution to the problem of information overload is the recommendation system, which is a personalized information recommendation system that recommends the information and products that the user is interested in to the user according to the user's information needs and interests. ...

AI Technical Summary

Problems solved by technology

If you directly use the author item set to calculate the frequent pattern, there will be some problems. For example, there are three author item sets: {Li Qi, Wang Lei, Li Long}, {Li Qi, Wang Lei, Song Jia} and {Wang Lei, Yang Zhi, Zhang Min }, when the confidence level is set to 2, we can get the frequent item {Li Qi, Wang Lei}. If we want to get the documents corresponding to this item set, we have to scan all the documents, although some indexing techniques can be used to reduce Scan time complexity

But it will also increase the cost of space overhead and maintenance

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