文献类型：期刊文献

英文题名：A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

作者：Wang, Xibin Dai, Zhenyu Li, Hui Yang, Jianfeng

第一作者：Wang, Xibin

通信作者：Li, H[1]

机构：[1]Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Guizhou, Peoples R China;[2]Special Key Lab Artificial Intelligence & Intelli, Guiyang 550003, Guizhou, Peoples R China;[3]Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China.

年份：2020

卷号：2020

外文期刊名：DISCRETE DYNAMICS IN NATURE AND SOCIETY

收录：;Scopus(收录号：2-s2.0-85096503213);WOS:【SCI-EXPANDED(收录号:WOS:000594207900001)】；

基金：This work was partially supported by the Technology Foundation of Guizhou Province (grant no. QianKeHeJiChu [2020]1Y269), New Academic Seedling Cultivation and Exploration Innovation Project (grant no. QianKeHe Platform Talents[2017]5789-21), Program for Innovative Talent of Guizhou Province (grant no. QianCaiJiao [2018]190), National Natural Science Foundation of China (grant nos. 71901078 and 71964009), High-Level Talent Project of Guizhou Institute of Technology (grant no. XJGC20190929), and Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province (grant no. KY [2020]001).

语种：英文

摘要：In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a "maximum-minimum segmentation" of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.