作者机构：
[Ye, Jiaxin; Meng, Xuan; Xiong, Huixiang] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Guo, Jinpeng] Cent China Normal Univ, Sch Polit & Int Studies, Wuhan, Peoples R China.
关键词：
Group recommendation;Comment function classification;Comment role classification;Book
摘要：
Purpose
The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web.
Design/methodology/approach
The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations.
Findings
Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments.
Originality/value
Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.
作者机构：
[Jing Chen; Lu Zhang] School of Information Management, Central China Normal University, Wuhan 430079, China;Institute of Digital Commerce, Wuhan Technology and Business University, Wuhan 430065, China;Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China;[Hui Liu] Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China;[Shuaipu Chen] School of Information Management, Wuhan University, Wuhan 430072, China
通讯机构：
[Quan Lu] I;Institute of Digital Commerce, Wuhan Technology and Business University, Wuhan 430065, China<&wdkj&>Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
关键词：
Predicting information usefulness;Gaze;Gesture;Health information identification;Misinformation
摘要：
Finding useful health information should be the highest priority when identifying health information. Predicting information usefulness will significantly improve the effectiveness and efficiency of health information identification, which plays a vital role in fighting against misinformation. Modal behaviors, such as gesture and gaze, are promising indicators of usefulness since they deliver a reliable, thorough, natural, and direct process of user cognitive processing. Therefore, this study aimed to use gesture and gaze behaviors to predict whether information is useful for health information identification. Twenty-four college students were recruited to freely search for information using a smartphone to identify the truthfulness of four propositions (two were true and two were false) about public health epidemics. The participants' gesture behavior, gaze behavior, and information usefulness as perceived by themselves were collected. Based on user cognition, the process of information usefulness judgment was placed into two phases: skimming and reading. Thirty-one features derived from modal behaviors in each phase were extracted. Feature optimization based on the Mann-Whitney U test and random forest was performed. Five common algorithms were used to construct information usefulness prediction models, and these models were compared by the F1_score. Finally, dwell time and gaze entropy in the reading phase were the most important gesture and gaze features respectively. BP neural network was selected to build a unimodal model based on gesture, and gradient boosting decision tree was selected to build a unimodal model based on gaze and a multimodal model combining both. These models all achieved F1_score above 77% and were applicable to different scenarios in health information identification. The model based on gesture could satisfy strong technology or legal constrains, the model based on gaze was ideal for AR, MR or metaverse applications, and the model combining both offered an alternative for multimodal human-computer interaction.
摘要：
With the rise of climate disasters, consumers have growing interest in low-carbon products. Considering the information updating of low-carbon preferences, this paper analyses incentive strategies for low-carbon supply chains. Based on the public's increase in environmental awareness during the lead time of supply chains, this paper first describes information updating in low-carbon supply chains. Second, we analyze the response decisions of this supply chain under the given incentive strategies. Based on these decisions, an optimal model of carbon reduction is then designed for the government. Finally, these best incentive strategies (including promotion allowance and carbon reduction) are optimized using a heuristic algorithm. The numerical results reveal that cooperation of profit-driven supply chain members improves not only their profits but also carbon reduction efficiency. Governments should promote the coordination of low-carbon supply chains to realize win-win outcomes. In addition, a reasonably higher carbon reduction level and sale price can both help to weaken the bullwhip effect of the supply chain. Effective information updating improves the carbon reduction efficiency better than a promotion allowance.
