Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
3040-3046

Recommendation algorithm with rating context and item similarity

Lu Zelun1
Gu Wanrong1,2
Mao Yijun1
Chen Ziming1
1. College of Mathematics & Informatics, South China Agricultural University, Guangzhou 510642, China
2. Guangzhou Key Laboratory of Intelligent Agriculture, Guangzhou 510642, China
DOI: 10.19734/j.issn.1001-3695.2023.02.0057

Abstract

In the recommendation system, the user's ratings are often affected by the rating context, that is, the user's previous ratings of some items will affect the objectivity of his rating of the current item. Sparse linear method treats user ratings affected by context as the same as other ratings when calculating item similarity. However, this partial ratings cannot objectively reflect the similarity between items. To solve the above problems, this paper proposed a recommendation algorithm combining rating context and item similarity based on sparse linear method. It divided the algorithm into three stages. The first stage used weighted ratings to calculate the item's nearest neighbor for feature selection. In the second stage, it used the rating error weight to reduce the fitting of the ratings affected by the context of the algorithm model, and trained the item similarity matrix. In the third stage, it predicted the ratings according to the user's ratings and the item similarity, and finally sorted the predicted ratings to complete the item recommendation. Experiments were conducted on four datasets of MovieLens, it used mean average precision(MAP), mean reciprocal rank(MRR) and normalized discounted cumulative gain(NDCG) to evaluate the effectiveness of the algorithm. The experimental results show that the fusion rating context will further improve the accuracy of item similarity and thus improve the performance of recommendation.

Foundation Support

中山大学广东省计算科学重点实验室开放基金资助项目(2021010)
广东省自然科学基金面上项目(2022A1515011489)
国家社科基金后期资助项目(19FTJB001)
广东省哲学社会科学规划项目(GD19CGL34)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.02.0057
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Algorithm Research & Explore
Pages: 3040-3046
Serial Number: 1001-3695(2023)10-024-3040-07

Publish History

[2023-04-28] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

卢泽伦, 古万荣, 毛宜军, 等. 融合评分上下文和物品相似度的推荐算法 [J]. 计算机应用研究, 2023, 40 (10): 3040-3046. (Lu Zelun, Gu Wanrong, Mao Yijun, et al. Recommendation algorithm with rating context and item similarity [J]. Application Research of Computers, 2023, 40 (10): 3040-3046. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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