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The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.

Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.

This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.

This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

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.

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.

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.

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.

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

The purpose of this paper is to consider the role of the Mental Health Act (MHA) 1983 in safeguarding adults at risk of abuse and neglect. The author has undertaken a thematic review of Safeguarding Adults Reviews (SARs) commissioned in England and Adult Practice Reviews (APRs) commissioned in Wales where the MHA 1983 was a central aspect to the review.

Reviews were included based on specific determinants, following analysis of SARs, APRs and executive summaries. This should not affect the credibility of the research, as themes were identified in conjunction with analysis of literature regarding use of the MHA in the context of adult safeguarding. Consequently, this review has been underpinned by evidence-based research in the area of study.

The interaction between statutes, such as the MHA 1983 and Care Act 2014, signify challenges to professionals, with variable application of mental health legislation in practice.

Lack of a complete national repository for review reports means that it is likely that the data set analysis is incomplete. It was noted that limitations to this research include the fact that Safeguarding Adults Boards in England may not publish SAR reports or may choose to publish an executive summary or practice brief instead of the full SAR report, therefore limiting the scope of disseminating learning from SARs, as this is difficult to achieve where the full report has not been published. The author aimed to mitigate this by undertaking comprehensive searches of Local Authority and SAB websites, in addition to submitting Information requests to ensure that this research encompassed as many relevant review reports as possible.

This is an important and timely topic for debate, given that the UK Government is proposing reform of the MHA 1983. In addition, existing thematic reviews of SARS tend to be generalised, rather than specifically focused on the MHA.

The purpose of this paper is to update the core data set of self-neglect safeguarding adult reviews (SARs) and accompanying thematic analysis. The initial data set was published in this journal in 2015 and has since been updated annually. The complete data set is available from the author. The second purpose is to reflect on the narratives about adult safeguarding and self-neglect by focusing on the stories that are told and untold in the reviews.

Further published reviews are added to the core data set, drawn from the national SAR library and the websites of Safeguarding Adults Boards (SABs). Thematic analysis is updated using the domains used previously, direct work, the team around the person, organisational support and governance. SAR findings and recommendations are also critiqued using three further domains: knowledge production, explanation and aesthetics.

Familiar findings emerge from the thematic analysis and reinforce the evidence-base of good practice with individuals who self-neglect and for policies and procedures with which to support those practitioners working with such cases. SAR findings emphasise the knowledge domain, namely, what is actually found, rather than the explanatory domain that seeks to answer the question “why?” Findings and recommendations appear to assume that learning can be implemented within the existing architecture of services rather than challenging taken-for-granted assumptions about the context within which adult safeguarding is situated.

A national database of reviews completed by SABs has been established (www.nationalnetwork.org.uk), but this data set remains incomplete. Drawing together the findings from the reviews nonetheless reinforces what is known about the components of effective practice, and effective policy and organisational arrangements for practice. Although individual reviews might comment on good practice alongside shortfalls, there is little analysis that seeks to explain rather than just report findings.

Answering the question “why?” remains a significant challenge for SARs, where concerns about how agencies worked together prompted review but also where positive outcomes have been achieved. The findings confirm the relevance of the evidence-base for effective practice, but SARs are limited in their analysis of what enables and what obstructs the components of best practice. The challenge for SAR authors and for partners within SABs is to reflect on the stories that are told and those that remain untold or untellable. This is an exercise of power and of ethical and political decision-making.

The paper extends the thematic analysis of available reviews that focus on work with adults who self-neglect, further reinforcing the evidence base for practice. The paper analyses the degree to which SARs answer the question “why?” as opposed simply to answering the question “what?” It also explores the degree to which SARs appear to accept or challenge the context for adult safeguarding. The paper suggests that SABs and SAR authors should focus explicitly on what enables and what obstructs the realisation of best practice, and on the choices they make about the stories that are told.

Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper aims to investigate how recommendations from different social ties influence consumers’ purchase intentions through both behavior and brain activity.

Utilizing behavioral (N = 70) and electroencephalogram (EEG) (N = 49) experiments, this study explored participants’ behavior and brain responses after being recommended by different social ties. The data were analyzed using statistical inference and event-related potential (ERP) analysis.

Behavioral results show that social tie strength positively impacts purchase intention, which can be fitted by a logarithmic model. Moreover, recommender-to-customer similarity and product affect mediate the effect of tie strength on purchase intention serially. EEG findings show that recommendations from weak tie strength elicit larger N100, N200 and P300 amplitudes than those from strong tie strength. These results imply that weak tie strength may motivate individuals to recruit more mental resources in social recommendation, including unconscious processing of consumer attention and conscious processing of cognitive conflict and negative emotion.

This study considers the effects of continuous social ties on purchase intention and models them mathematically, exploring the intrinsic mechanisms by which strong and weak ties influence purchase intentions through recommender-to-customer similarity and product affect, contributing to the applications of the stimulus-organism-response (SOR) model in the field of social recommendation. Furthermore, our study adopting EEG techniques bridges the gap of relying solely on self-report by providing an avenue to obtain relatively objective findings about the consumers’ early-occurred (unconscious) attentional responses and late-occurred (conscious) cognitive and emotional responses in purchase decisions.

This study provides a meta-review of global virtual team (GVT)–related reviews, creating a resource that highlights dominant themes, research trends and shifts in topics over time culminating in a summary of opportunities for future research. By analyzing and grouping the evidence presented in previous research, this meta-review provides key insights toward future research and managerial implications.

This meta-review identifies 35 existing GVT-related reviews across 32 publication outlets, providing a longitudinal and cross-disciplinary view of GVT research to date.

Results of the analysis reveal over time that there has been a largely adopted reconceptualization of the GVT paradigm toward a continuum of virtuality. There has been a shift in the view of the cross-cultural and global components of GVTs toward a recognition that a greater variance of dimensionality exists. Additionally, popular themes across the literature emerge, notably, virtuality, concepts of culture, trust, leadership and communication technology.

As a multidisciplinary GVT-focused meta-review, this study complements previous efforts by taking a tour across this wide topic and is dedicated to those who are researching, teaching, working and managing GVT-related strategies. The reviews selected represent work published across multiple literature streams, providing a comprehensive and forward thinking perspective.

The existing technology adoption model (TAM) has ignored the importance of social proof of credibility such as risk, security and privacy. Also, this study aims to provide understanding how the issues of usability such as interactivity, compatibility and credibility can influence the mobile banking (MB) adoption of commercial and Islamic banks. This study has offered two theoretical models which may be helpful to understand the importance of social proof of credibility during MB adoption.

It is exploratory study therefore the researcher used triangulation of qualitative methods such as online banking app reviews, focused group and semi structured interviews are used to understand the behavior of customers toward MB adoption.

Consumers are actively involved to take recommendations from close sources, experts, customers and crowd opinion using interactive social media platforms (SMPs). The purpose to gather information is to save from usability, risk, security and privacy issues, especially when customers share their personal and financial information with the purpose to use MB. Results reveal that people with disabilities require MB app that has adjustable size, color, text and functions. The usability issues such as system design, response time, aesthetics, personalization, installation and service quality influenced the use of MB in Islamic banks.

Present study has offered social proof as TAM which highlights usefulness and credibility as important factors that can create social proof on SMPs. This study provides rich insights regarding what initiatives are required to resolve the issues of usability such as interactivity, compatibility and credibility that can influence the MB adoption of commercial and Islamic banks. Another major contribution of this study is to explore the issues of MB for people with disabilities and suggest how they can improve the service quality of MB for people with disabilities.