Open AccessResearch on Personalized Recommendation System Based on Association Rules
Author(s) -
Haiyan Song,
Huan Zhang,
Zhihao Xing
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 - 10.1088/1742-6596/1961/1/012027
Subject(s) - movielens , computer science , collaborative filtering , recommender system , association rule learning , set (abstract data type) , scheme (mathematics) , data mining , focus (optics) , information retrieval , data science , mathematical analysis , mathematics , programming language , physics , optics
With the rapid development of network technology, the amount of information is rapidly expanding, and the amount of various data has become huge and scattered. Using traditional keywords to retrieve search data has become quite time-consuming and difficult to focus on. Traditional search engines have been unable to help people effectively solve this problem, so personalized recommendation systems have emerged. In view of the user’s behavior and preferences, this article makes a detailed comparison of different recommendation technologies. It focuses on analyzing the applicability of the current popular content-based, association rules, and collaborative filtering recommendation technologies to the current needs. The technical scheme and system structure of recommendation system based on association rules are proposed, and aiming at the shortcomings of traditional recommendation system algorithms (the algorithm’s sparsity problem) and the current cold start problem of the recommendation system. Based on the MovieLens data set, a series of evaluation indicators are set up, comparative experiments are carried out, and the effectiveness of the proposed improved algorithm is analyzed.