| 摘 要: 传统推荐系统算法模型主要集中研究用户偏好与物品的关联性，根据用户主观意见进行推荐，未充分考虑用户与物品所处的客观环境，造成推荐时的实际偏差。本文基于传统推荐算法引入时间因子，提高模型推荐效果。实现方法主要是通过比较引入与未引入时间因子，使用UserCF算法和ItemCF算法观察MAE值的大小变化情况。时间因子的引入，改善了传统推荐系统算法模型主要集中研究用户偏好与物品的关联性等方面的推荐失真问题，提高了模型推荐的可靠性和实用性。实验结果表明，引入时间因子能对传统协同过滤算法在MAE指标方面有一定提高，计算效果优于传统推荐算法。 |
| 关键词: 时间因子；个性化推荐；协同过滤 |
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中图分类号: TP311.60
文献标识码: A
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| 基金项目: 广东教育学会“十三五”教育科研课题“大数据”背景下教学资源云平台建设与研究项目(GDES13614)；广州理工学院2020校级科研项目(2020KY003)；2021年广州市高等学校第十一批教育教学改革研究项目(2021JG107). |
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| Improved Personalized Recommendation Model based on Time Factor |
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HU Anming1, CHEN Huie2
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( 1.School of Computer Science and Engineering, Guangzhou Institute of Technology, Guangzhou 510540, China;
2.Guangdong University of Finance, Guangzhou 510521, China)
anminghu@qq.com; 318802207@qq.com
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| Abstract: Traditional model of recommendation system algorithm mainly focuses on the relationship between user preferences and items, and makes recommendations according to users' subjective opinions. It fails to take into full consideration the objective environment of the user and the item, resulting in actual deviation in recommendation. This paper proposes to improve model recommendation effect by introducing time factor into traditional recommendation algorithm. The improved model is realized by comparing algorithms with and without time factor, and using UserCF algorithm and ItemCF algorithm to observe the changes of MAE (Mean Absolute Error) values. Introduction of time factor improves the algorithm model of traditional recommendation system, which mainly focuses on the recommendation distortion of user preferences and the relevance of items, so to improve the reliability and practicability of the model recommendation. Experimental results show that introduction of time factor can improve MAE index of traditional collaborative filtering algorithm, and calculation effect is better than that of traditional recommendation algorithm. |
| Keywords: time factor; personalized recommendation; collaborative filtering |
