[1]梁丽君,李业刚,张娜娜,等.融合用户特征优化聚类的协同过滤算法[J].智能系统学报,2020,15(6):1091-1096.[doi:10.11992/tis.201710024]
LIANG Lijun,LI Yegang,ZHANG Nana,et al.Collaborative filtering algorithm combining user features and preferences in optimized clustering[J].CAAI Transactions on Intelligent Systems,2020,15(6):1091-1096.[doi:10.11992/tis.201710024]
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LIANG Lijun,LI Yegang,ZHANG Nana,et al.Collaborative filtering algorithm combining user features and preferences in optimized clustering[J].CAAI Transactions on Intelligent Systems,2020,15(6):1091-1096.[doi:10.11992/tis.201710024]
融合用户特征优化聚类的协同过滤算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第6期
页码:
1091-1096
栏目:
学术论文—人工智能基础
出版日期:
2020-11-05
- Title:
- Collaborative filtering algorithm combining user features and preferences in optimized clustering
- Keywords:
- recommendation system; collaborative filtering; cold start; scalability; optimization clustering; trust degree; user attribute; user preference
- 分类号:
- TP311
- 摘要:
- 针对推荐系统领域中应用最广泛的协同过滤推荐算法仍伴随着数据稀疏性、冷启动和扩展性问题，基于用户冷启动和扩展性问题，提出了基于改进聚类的PCEDS(pearson correlation coefficient and euclidean distance similarity)协同过滤推荐算法。首先针对用户属性特征，采用优化的K-means聚类算法对其聚类，然后结合基于信任度的用户属性特征相似度模型和用户偏好相似度模型，形成一种新颖的PCEDS相似度模型，对聚类结果建立预测模型。实验结果表明：提出的PCEDS算法比传统的协同过滤推荐算法在均方根误差(RMSE)上降低5%左右，并且推荐准确率(precision)和召回率(recall)均有明显提高，缓解了冷启动问题，同时聚类技术可以节省系统内存计算空间，从而提高了推荐效率。
- Abstract:
- The collaborative filtering recommendation algorithm in the field of recommendation systems is still accompanied by the data sparsity, cold start, and scalability problems. To solve the cold start and scalability problems, we propose a PCEDS(pearson correlation coefficient and euclidean distance) collaborative filtering recommendation algorithm based on optimized clustering. First, the optimized K-means clustering algorithm is used to cluster the attributes of users. Then, based on the trust-based similarity model of user attribute features and the similarity model of user preference, a novel PCEDS similarity model is established to create a prediction model for the clustering results. The experimental results indicate that, compared with the traditional collaborative filtering recommendation algorithm, the proposed PCEDS collaborative filtering recommendation algorithm reduces the root mean square error by approximately 5%, significantly improves the recommendation precision and recall, and solves the cold start problem. Simultaneously, the clustering technology can save the memory space of the recommendation system, thereby improving its efficiency.
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备注/Memo
收稿日期:2017-10-29。
基金项目:国家自然科学基金项目(61671064)
作者简介:梁丽君，硕士研究生，主要研究方向为个性化推荐系统;李业刚，副教授，博士，中文信息学会会员，主要研究方向为语言信息处理、机器学习、机器翻译、社交网络和跨语言信息检索。申请发明专利多项，发表学术论文10余篇;张娜娜，硕士研究生，主要研究方向为自然语言处理
通讯作者:李业刚.E-mail:liyegang@sdut.edu.cn
更新日期/Last Update:
2020-12-25