[1]徐新卫,陶 飞,邓佳佳,等.融合 FCM 和 TFNs 的协同过滤推荐算法[J].计算机技术与发展,2023,33(03):161-166.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 024]
　XU Xin-wei,TAO Fei,DENG Jia-jia,et al.Collaborative Filtering Recommendation Algorithm Incorporating FCM and TFNs[J].,2023,33(03):161-166.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 024]
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融合 FCM 和 TFNs 的协同过滤推荐算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年03期
页码:
161-166
栏目:
人工智能
出版日期:
2023-03-10

文章信息/Info

Title:
Collaborative Filtering Recommendation Algorithm Incorporating FCM and TFNs
文章编号:
1673-629X(2023)03-0161-06
作者:
徐新卫1 ; 陶 飞1 ; 邓佳佳1 ; 周 俊2
1. 安徽工业大学 管理科学与工程学院,安徽 马鞍山 243032;
2. 广东科技学院,广东 东莞 523073
Author(s):
XU Xin-wei1 ; TAO Fei1 ; DENG Jia-jia1 ; ZHOU Jun2
1. School of Management Science and Engineering,Anhui University of Technology,Maanshan 243032,China;
2. Guangdong Institute of Science and Technology,Dongguan 523073,China
关键词:
协同过滤; 梯形模糊数; 模糊 C 均值; 遗传算法; 离散评分
Keywords:
collaborative filtering; trapezoidal fuzzy number; fuzzy C-means; genetic algorithm; discrete scoring
分类号:
TP391. 3
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 03. 024
摘要:
针对推荐算法中的稀疏性问题和传统推荐系统中使用离散评分,用户对物品的喜好程度只能通过 5 个等级来选取,用户对物品的偏好程度是模糊的且 5 等级评分不能合理表达用户的喜好,提出一种结合模糊 C 均值( Fuzzy C-Means,FCM) 和梯形模糊数( Trapezoidal Fuzzy Numbers,TFNs) 的协同过滤算法。 首先,在传统的模糊 C 均值算法上融合遗传算法,将遗传算法的搜索结果作为模糊 C 均值的初始聚类中心,以其克服传统 FCM 搜索极易陷入局部最小值点的缺陷;然后,引入梯形模糊相似度模型,将离散评分数转化为梯形模糊数以此来计算用户相似度,从而利用模糊分数预测估计进行推荐;最后,选取 MAE 和 RMSE 作为评估指标,在 Movielens 数据集中进行实验,实验结果显示所提算法在与其余四种算法对比中预测误差更低,精确度更高,有效提高了推荐质量,也证明了该算法对于稀疏性问题有一定程度上的缓解,表明了该算法的有效性。
Abstract:
In view of the sparsity problem in recommendation algorithms and the discrete ratings used in traditional recommendationsystems,the user’ s preference for items only be selected by 5 levels,the fuzzy user’s preference for items and the 5 -level rating cannotreasonably express the user爷 s preference,a collaborative filtering algorithm combining Fuzzy C-Means ( FCM) and Trapezoidal FuzzyNumbers ( TFNs) is proposed. Firstly,the genetic algorithm is integrated with the traditional fuzzy c-means algorithm,and the searchresult of the genetic algorithm is used as the initial clustering center of the fuzzy c-means to overcome the defect that the traditional FCMsearch is quite easy to fall into the local minima. Then the trapezoidal fuzzy similarity model is introduced,and the discrete score numbersare transformed into trapezoidal fuzzy numbers to calculate the user similarity,so that the fuzzy score prediction estimation can be used forrecommendation. Finally,MAE and RMSE are selected as evaluation indexes and experiments are conducted in Movielens dataset. It isshowed that the proposed algorithm has lower prediction error and higher accuracy in comparison with the remaining four algorithms,which effectively improves the recommendation quality and also proves that the algorithm has a certain degree of alleviation for thesparsity problem,indicating its effectiveness.

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更新日期/Last Update: 2023-03-10