摘要：
BACKGROUND: The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. OBJECTIVE: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. METHODS: Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. RESULTS: A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. CONCLUSIONS: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.
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.
期刊：
Journal of Information Science,2023年 ISSN：0165-5515
通讯作者：
Weihua Deng
作者机构：
[Ming Yi; Ming Liu; Cuicui Feng] School of Information Management, Central China Normal University, China;[Weihua Deng] College of Public Administration, Huazhong Agricultural University, China
通讯机构：
[Weihua Deng] C;College of Public Administration, Huazhong Agricultural University, China
关键词：
Cross-domain recommendation;heterogeneous graph neural network;high-order information;rating information
摘要：
Cross-domain recommendation models are proposed to enrich the knowledge in the target domain by taking advantage of the data in the auxiliary domain to mitigate sparsity and cold-start user problems. However, most of the existing cross-domain recommendation models are dependent on rating information of items, ignoring high-order information contained in the graph data structure. In this study, we develop a novel cross-domain recommendation model by unified modelling high-order information and rating information to tackle the research gaps. Different from previous research work, we apply heterogeneous graph neural network to extract high-order information among users, items and features; obtain high-order information embeddings of users and items; and then use neural network to extract rating information and obtain user rating information embeddings by a non-linear mapping function MLP (Multilayer Perceptron). Moreover, high-order information embeddings and rating information embeddings are fused in a unified way to complete the final rating prediction, and the gradient descent method is adopted to learn the parameters of the model based on the loss function. Experiments conducted on two real-world data sets including 3,032,642 ratings from two experimental scenarios demonstrate that our model can effectively alleviate the problems of sparsity and cold-start users simultaneously, and significantly outperforms the baseline models using a variety of recommendation accuracy metrics.
作者机构：
[Cao, Shiyang] Shanxi Univ Finance & Econ, Int Exchange & Cooperat Dept, Taiyuan, Peoples R China.;[Ma, Xiao] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.;[Yi, Ming; Zeng, Jiangfeng] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Zeng, Jiangfeng] Ctr Data Governance & Intelligent Decis Making Hub, Wuhan, Peoples R China.
通讯机构：
[Zeng, JF ] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;Ctr Data Governance & Intelligent Decis Making Hub, Wuhan, Peoples R China.
关键词：
Financial sentiment analysis;Fresh and hot opinions;Temporal modeling;Fresh-hot bilinear pooling
摘要：
Financial sentiment analysis aims to extract public opinion about an institution to help financial researchers make better decisions. To predict sentiment more accurately, it is necessary for models to improve their capability to capture long-term temporal information and support multi-user interaction. However, existing methods only analyze sentiment based on one comment from a user, which fails to fully exploit the latent emotions of the public, and they lack effective temporal modeling and interaction capabilities. In this paper, we analyze a company from two perspectives to alleviate the above issues: (1) the fresh opinions can reflect timely public attitudes towards a company, while (2) the hot opinions provide the most influential views. A comprehensive exploration of fresh and hot financial sentiment can help researchers make more accurate determinations. To this end, we propose a novel financial sentiment classification framework (FSCN), that can capture temporal information and interact with the opinions of users to make a more comprehensive decision. Our approach takes into account the inherent temporal dependencies in public opinions and combines both views of information to achieve an accurate classification of financial sentiment. Specifically, the FSCN contains (1) a multi-opinion extractor to filter and extract features from massively fresh and hot opinions, respectively. (2) a fresh-hot bilinear pooling (FHBP) module to effectively fuse fresh and hot features. Additionally, to verify the effectiveness of the proposed method, we crawl data from the Internet and create a real-world public opinion dataset that consists of 79,350 comments from 837 companies. Extensive experiments demonstrate that our framework achieves state-of-the-art results on this real-world dataset and is capable of providing reliable service in the financial system. Codes will be released at https://github.com/zjfgh2015/FSCN .
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.
