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Improved collaborative filtering algorithm for recommendation system

A collaborative filtering algorithm and recommendation system technology, applied in the field of improving collaborative filtering algorithms, can solve problems such as shortening the execution time of the algorithm

Pending Publication Date: 2022-04-05
HOHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Third, the use of distributed computing improves the execution efficiency of the collaborative filtering recommendation algorithm, solves the problem of excessively long algorithm execution time caused by excessive data sets, and uses Spark's memory-based parallel computing to greatly shorten the algorithm time. Execution time, improving user experience

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  • Improved collaborative filtering algorithm for recommendation system
  • Improved collaborative filtering algorithm for recommendation system
  • Improved collaborative filtering algorithm for recommendation system

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Embodiment Construction

[0030] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0031] Taking the scheme shown in the accompanying drawings as an example, the preferred implementation is as follows:

[0032] 1. The main flow of the algorithm

[0033] Step 1: Dataset construction: Split and convert the dataset in the form of a file into a database for preprocessing, and convert it into a form that can be directly used by the algorithm.

[0034] Step 2: optimize the storage scheme: adopt triplets to store the effective values ​​in the sparse matrix;

[0035] Step 3: This step is divided into two parts

[0036] The first part: the main steps of the recommendation algorithm based on knowledge graph, including:

[0037] (1) Build a movie knowledge map: build a movie knowledge map based on basic information such as movie ratings and labels;

[0038] (2) Learning the connotative semantic knowledge of movies: According to the...

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Abstract

The invention belongs to the field of movie recommendation system application, provides an improved collaborative filtering algorithm oriented to a recommendation system, and provides a recommendation algorithm fusing a knowledge graph and collaborative filtering for the cold start problem and the sparsity problem on the basis of the collaborative filtering algorithm. Semantic information in the knowledge graph is used as auxiliary information to be fused into a collaborative filtering algorithm, so that the degree of dependence of the algorithm on scores is reduced; meanwhile, the invention provides a triple storage structure to store a sparse matrix, the problem of space waste caused by a traditional two-dimensional sparse matrix can be effectively relieved, the space efficiency of a recommendation algorithm is improved, the triple is stored by using a hash algorithm, and the access speed and access efficiency of a certain triple are improved; moreover, distributed calculation is adopted, the problem that the execution time is too long due to the fact that a data set is too large is solved, and the execution time of the algorithm is greatly shortened by means of memory-based parallel calculation of Spark.

Description

technical field [0001] The invention belongs to the application field of a movie recommendation system, and proposes an improved collaborative filtering algorithm oriented to the recommendation system. Background technique [0002] With the rapid development of Internet technology, the amount of global data has exploded. The rapid growth of information on the Internet, on the one hand, enables people to obtain more and more information resources, which brings great convenience to people; on the other hand, facing the massive information resources, people have to spend more time and energy To search for helpful information, so the phenomenon of "information overload (Information Overload)" is becoming more and more serious. The recommendation system can effectively solve the problem of information overload. According to the characteristics of users, it recommends project objects that meet their needs for users and realizes personalized services. The advantage of the recomme...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/36
Inventor 唐伟杰韩立新
Owner HOHAI UNIV