文献类型：期刊文献

英文题名：Enhancing Personalized Recommendation by Transductive Support Vector Machine and Active Learning

作者：Wang, Xibin Li, Yunji Chen, Jing Yang, Jianfeng

第一作者：Wang, Xibin

通信作者：Wang, XB[1];Wang, XB[2];Wang, XB[3]

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]Key Lab Elect Power Big Data Guizhou Prov, Guiyang 550003, Guizhou, Peoples R China;[4]Guizhou Univ Tradit Chinese Med, Coll Informat Engn, Guiyang 550025, Guizhou, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Guizhou, Peoples R China;corresponding author), Special Key Lab Artificial Intelligence & Intelli, Guiyang 550003, Guizhou, Peoples R China;corresponding author), Key Lab Elect Power Big Data Guizhou Prov, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院;

年份：2022

卷号：2022

外文期刊名：SECURITY AND COMMUNICATION NETWORKS

收录：;EI(收录号：20221311851493);Scopus(收录号：2-s2.0-85126959897);WOS:【SCI-EXPANDED(收录号:WOS:000773708400001)】；

基金：This work was partially supported by the National Natural Science Foundation of China (grant nos. 72161005, 71901078, and 71964009), Technology Foundation of Guizhou Province (grant nos. QianKeHeJiChu[2020]1Y269 and [2018]1068), 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

外文关键词：Information services - Population statistics - Recommender systems - Support vector machines

摘要：As an important component of information service networks, personalized recommendation technology provides users with better options and enables them to obtain information anytime and anywhere. Collaborative filtering (CF) is a successful and widely used form of this technology. However, the traditional CF recommendation algorithm is ineffective in environments with frequent entry of new users and high levels of data sparsity. For new users in the system, few or no scores, labels, or other such information is available, leading to the user cold start problem. Simultaneously, data sparsity leads to the selection of unreasonable neighbors, which reduces the recommendation accuracy. In addition, the traditional CF recommendation algorithm ignores the inherent connections between users' preferences and their basic information (such as demographics). Users with similar demographic information are likely to have similar preferences, which can serve as a good basis for finding neighbors. To address the aforementioned problems, we propose a recommendation model that combines active learning (AL) and a semi-supervised transductive support vector machine (TSVM). To enable neighbors to be found quickly and accurately, similar users are clustered together on the basis of their basic information. Then, the TSVM-based classifier is trained on each cluster. To improve the quality of sample labeling and thus the classifier performance, an active learning method based on the distance strategy and a multiclassifier voting mechanism is implemented. Finally, the TSVM-based recommendation model is trained on the labeled samples. The extensive experiments conducted using a real data set from MovieLens demonstrate that the proposed model effectively alleviates the aforementioned cold start and data sparsity problems.