文献类型：期刊文献

英文题名：Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis

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：2021

卷号：2021

外文期刊名：COMPLEXITY

收录：;EI(收录号：20211310132149);Scopus(收录号：2-s2.0-85102973041);WOS:【SCI-EXPANDED(收录号:WOS:000631901200006)】；

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

外文关键词：K-means clustering - Quality control - Sentiment analysis - Signal filtering and prediction

摘要：In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users' sentiment tendency by analysis of user review features is proposed to mine users' attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.