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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 purpose of this paper is to develop and validate an innovative Fuzzy Recognition Based‐Benefit Estimation Model (FRB‐BEM) to quantify the benefits obtained from a Mid‐Project Review (MPR) (e.g. the Gateway Review Process (GRP)). This is a quantitative assessment to evaluate the benefits obtained from conducting MPRs. With the wide adoption of MPR internationally, such measurements will better support critical decisions in capital projects and also assist to optimize project lifecycle performance.

This paper adopted Relative Membership Degree (RMD) based fuzzy sets as the fundamental theory to develop the FRB‐BEM utilizing linguistic information from MPR reports. It was then tested by analysis of an aviation IT project that underwent a Gateway review. A parametric study was also conducted to calibrate the model.

The FRB‐BEM developed and validated in this paper provided a viable approach to quantify the total benefits obtained from undertaking MPRs.

Refinement of the FRB‐BEM assumptions would benefit from testing against a wide project sample set.

Using the FRB‐BEM applications to better demonstrate the benefits of MPRs.

The paper demonstrates how FRB‐BEM has extended RMD based fuzzy sets theory into applications for MPRs and incorporated fuzzy level values based on linguistic interpretation of hard data.

Effective and robust governance of major projects and programmes in the public sector is crucial to the accountability of the state and the transparency of state spending. The theoretical discourse on governance, in the context of projects and programmes, is not fully mature, although is now sufficiently well developed to warrant an increased scholarly focus on practice. This paper aims to contribute to the empirical literature through a study of assurance routines in the UK Government Major Projects Portfolio (GMPP).

A framework analysis approach to the evaluation of a subset of GMPP database generates original insights into (1) the framing of assurance review recommendations, (2) the treatment of assurance review data and (3) the subsequent tracking of the implementation of actions arising from the assurance review process.

The analysis reveals that the “delivery confidence” of the major projects and programmes included in this study improves during the time that they are assured on the GMPP. This would suggest that “enhanced” governance routines are desirable in programmes and projects that exhibit high degrees of complexity and scale.

The research findings contribute to the wider conversations in this journal and elsewhere on project governance routines and governance-as-practice in the context of government and public services.

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.

The purpose of this paper is to update the core data set of self-neglect safeguarding adult reviews (SARs) and accompanying thematic analysis, and to address the challenge of change, exploring the necessary components beyond an action plan to ensure that findings and recommendations are embedded in policy and practice.

Further published reviews are added to the core data set from the websites of Safeguarding Adults Boards (SABs). Thematic analysis is updated using the four domains employed previously. The repetitive nature of the findings prompts questions about how to embed policy and practice change, to ensure impactful use of learning from SARs. A framework for taking forward an action plan derived from an SAR findings and recommendations is presented.

Familiar, even repetitive findings emerge once again from the thematic analysis. This level of analysis enables an understanding of both local geography and the national legal, policy and financial climate within which it sits. Such learning is valuable in itself, contributing to the evidence base of what good practice with adults who self-neglect looks like. However, to avoid the accusation that lessons are not learned, something more than a straightforward action plan to implement the recommendations is necessary. A framework is conceptualised for a strategic and longer-term approach to embedding policy and practice change.

There is still no national database of reviews commissioned by SABs so the data set reported here might be incomplete. The Care Act 2014 does not require publication of reports but only a summary of findings and recommendations in SAB annual reports. This makes learning for service improvement challenging. Reading the reviews reported here enables conclusions to be reached about issues to address locally and nationally to transform adult safeguarding policy and practice.

Answering the question “how to create sustainable change” is a significant challenge for SARs. A framework is presented here, drawn from research on change management and learning from the review process itself. The critique of serious case reviews challenges those now engaged in SARs to reflect on how transformational change can be achieved to improve the quality of adult safeguarding policy and practice.

The paper extends the thematic analysis of available reviews that focus on work with adults who self-neglect, further building on the evidence base for practice. The paper also contributes new perspectives to the process of following up SARs by using the findings and recommendations systematically within a framework designed to embed change in policy and practice.

In 2006, the United Nations’ Human Rights Council was tasked to establish a new human rights monitoring mechanism: the universal periodic review process. The purpose of this paper is to examine the nature of discussions held in the process, over the two cycles of review in relation to women’s rights to access health care services.

This investigation is a documentary analysis of the reports of 193 United Nations’ state reports, over two cycles of review.

The primary findings of this investigation reveal that despite an apparent consensus on the issue, a deeper analysis of the discussions suggests that the dialogue between states is superficial in nature, with limited commitments made by states under review in furthering the protection of women’s right to access health care services in the domestic context.

Considering the optimism surrounding the UPR process, the findings reveal that the nature of discussions held on women’s rights to health care services is at best a missed opportunity to make a significant impact to initiate, and inform, changes to practices on the issue in the domestic context; and at worst, raises doubts as to whether the core aim of the process, to improve the protection and promotion of all human rights on the ground, is being fulfilled.

Deviating from the solely technocratic analysis of the review process in the existing literature, this investigation has considered the UPR process as a phenomenon of exploration in itself, and will provide a unique insight as to how this innovative monitoring mechanism operates in practice, with a particular focus on women’s right to access health care services.

The purpose of this paper is twofold: first, to update the core data set of self-neglect serious case reviews (SCRs) and safeguarding adult reviews (SARs), and accompanying thematic analysis; second, to respond to the critique in the Wood Report of SCRs commissioned by Local Safeguarding Children Boards (LSCBs) by exploring the degree to which the reviews scrutinised here can transform and improve the quality of adult safeguarding practice.

Further published reviews are added to the core data set from the websites of Safeguarding Adults Boards (SABs) and from contacts with SAB independent chairs and business managers. Thematic analysis is updated using the four domains employed previously. The findings are then further used to respond to the critique in the Wood Report of SCRs commissioned by LSCBs, with implications discussed for Safeguarding Adult Boards.

Thematic analysis within and recommendations from reviews have tended to focus on the micro context, namely, what takes place between individual practitioners, their teams and adults who self-neglect. This level of analysis enables an understanding of local geography. However, there are other wider systems that impact on and influence this work. If review findings and recommendations are to fully answer the question “why”, systemic analysis should appreciate the influence of national geography. Review findings and recommendations may also be used to contest the critique of reviews, namely, that they fail to engage practitioners, are insufficiently systemic and of variable quality, and generate repetitive findings from which lessons are not learned.

There is still no national database of reviews commissioned by SABs so the data set reported here might be incomplete. The Care Act 2014 does not require publication of reports but only a summary of findings and recommendations in SAB annual reports. This makes learning for service improvement challenging. Reading the reviews reported here against the strands in the critique of SCRs enables conclusions to be reached about their potential to transform adult safeguarding policy and practice.

Answering the question “why” is a significant challenge for SARs. Different approaches have been recommended, some rooted in systems theory. The critique of SCRs challenges those now engaged in SARs to reflect on how transformational change can be achieved to improve the quality of adult safeguarding policy and practice.

The paper extends the thematic analysis of available reviews that focus on work with adults who self-neglect, further building on the evidence base for practice. The paper also contributes new perspectives to the process of conducting SARs by using the analysis of themes and recommendations within this data set to evaluate the critique that reviews are insufficiently systemic, fail to engage those involved in reviewed cases and in their repetitive conclusions demonstrate that lessons are not being learned.

In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference.

The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations.

Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user.

This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.