Open AccessUser-based Collaborative Filtering Algorithm Design and Implementation
Author(s) -
Hulong Wang,
Zesheng Shen,
Jiang Shu-zhen,
Guang Sun,
RenJie Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012168
Subject(s) - computer science , collaborative filtering , information overload , the internet , algorithm , recommender system , personalization , software , information filtering system , data mining , machine learning , world wide web , programming language
With the rapid development of social humanities and technology, especially the development of the Internet, the current Internet era, the amount of information is increasing rapidly with explosive growth rate, the information overload problem is particularly obvious, on the accurate acquisition of information from the massive amount of information users want, from which the personalized recommendation technology was born. In order to solve the problem of acquiring the information users want, this paper researches and discusses a kind of personalized recommendation algorithm - a user-based collaborative filtering algorithm, analyzing the user behavior, comparing the advantages and disadvantages of other related algorithms, using the UserCF algorithm, and optimizing the sparse matrix to reduce the time and complexity of the operation. The algorithm is implemented by software to generate recommendation results. The results of the experimental data in this paper show that the algorithm is effective in recommending projects to users.