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This paper aims to make it convenient for those who have only just begun their research into Community Question Answering (CQA) expert recommendation, and for those who are already concerned with this issue, to ease the extension of our understanding with future research.

In this paper, keywords such as “CQA”, “Social Question Answering”, “expert recommendation”, “question routing” and “expert finding” are used to search major digital libraries. The final sample includes a list of 83 relevant articles authored in academia as well as industry that have been published from January 1, 2008 to March 1, 2019.

This study proposes a comprehensive framework to categorize extant studies into three broad areas of CQA expert recommendation research: understanding profile modeling, recommendation approaches and recommendation system impacts.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, it was found that conflicting and contradictory research results and research gaps in the existing research, and then put forward the urgent research topics.

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

The created portfolio is comparable and even profitable than several major global stock indices.

This work aims to benefit individual investors, investment managers and market onlookers.

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

Few library studies have investigated recommendation classifications for e‐book (electronic book) usage, while none have directly compared what recommendation sources (word‐of‐mouth, advertising, and expert recommendation) might influence e‐book usage intentions. To fill this gap in the literature, the main purposes of this study are to: examine how users perceive the influence of recommendations on the intention to use e‐books for academic purposes; and to measure the level of the perception of trust and perceived risk when users receive e‐book recommendations from peers, advertisers, and experts.

Data for this study were collected from 382 academic digital library users between the ages of 18 and 25. A multiple regression analysis was then conducted to identify the key causal relationships.

The comparison of three recommendation sources (word‐of‐mouth, advertising, and expert recommendations) revealed that word‐of‐mouth (WOM) played a more important role than other recommendations in determining the intention to use e‐books in an academic digital library. In addition, enhancing the perceived trust and reducing the risk towards the use of e‐books can mediate the relationship between recommendation sources and the behavioural intentions to use e‐books.

This study assessed self‐reported behavioural intention as part of its survey and, as a result, could have introduced unintentional inaccuracies.

Librarians should emphasise e‐book advantages (e.g. easy searching, easily accessible index) to get positive recommendation if users follow all of the recommendations of the source. They can also create online discussion forums to provide usage intention discussions, which can influence users' perceptions of trust and risk and increase the willingness of potential users to read e‐books.

Little has been written on the intentions of using e‐books. Therefore, this conceptual model is novel. This model is also useful in explaining how recommendations stimulate the intentions of using e‐books by enhancing the perceived trust and reducing the perceived risk; these findings may generally be applicable to librarians, current users, and potential users.

Expert Cloud as a new class of cloud systems enables its users to request and share the skill, knowledge and expertise of people by employing internet infrastructures and cloud concepts. Since offering the most appropriate expertise to the customer is one of the clear objectives in Expert Cloud, colleague recommendation is a necessary part of it. So, the purpose of this paper is to develop a colleague recommender system for the Expert Cloud using features matrices of colleagues.

The new method is described in two phases. In the first phase, all possible colleagues of the user are found through the filtering mechanism and next features of the user and possible colleagues are calculated and collected in matrices. Six potential features of colleagues including reputation, expertise, trust, agility, cost and field of study were proposed. In the second phase, the final score is calculated for every possible colleague and then top-k colleagues are extracted among users. The survey was conducted using a simulation in MATLAB Software. Data were collected from Expert Cloud website. The method was tested using evaluating metrics such as precision, accuracy, incorrect recommendation and runtime.

The results of this study indicate that considering more features of colleagues has a positive impact on increasing the precision and accuracy of recommending new colleagues. Also, the proposed method has a better result in reducing incorrect recommendation.

In this paper, the colleague recommendation issue in the Expert Cloud is pointed out and the solution approach is applied into the Expert Cloud website.

In organizations, knowledge intensive activities are mainly task oriented. Finding relevant completed tasks to the new task and providing task-related knowledge to workers facilitate the knowledge reuse. However, relevant tasks are not easily found in the huge amount of completed tasks. The purpose of this paper is to assist the worker to find the required knowledge for the task at hand by reusing the knowledge related to relevant competed tasks.

First, the task profile is constructed. Relevant degrees to categories which tasks to are derived by multi-granularity fuzzy linguistic method. The stages of completed tasks are identified by the modified KNN method. Second, similar completed tasks on categories are retrieved and then the relevant tasks are selected from the retrieved similar tasks by multi-granularity fuzzy linguistic method. Third, the worker’s current task stage is derived by calculating the similarity between the rated knowledge and the knowledge in the stage of completed tasks. Finally, the knowledge is recommend based on stage relevance, relevance of the completed tasks and importance of the knowledge.

The proposed method helps the worker find the knowledge related to the task at hand by finding and reusing the completed tasks. The experimental results show that the proposed method performs well and can fulfill the worker’s’ knowledge needs. The use of the linguistic term set with preferred granularities instead of precise numbers facilitates the expression of the opinions. The recommendation stage by stage makes the knowledge recommended more precisely. The obtained linguistic weight of the knowledge makes the recommended results understood more easily than the numerical values.

In the study, the authors just focus on the codified knowledge recommendation. However, there is another kind of knowledge named tacit knowledge, which exists in the mind of the experts. The constructing and updating of the expert profile can be investigated. Meanwhile, the new recommendation method which considers more factors also needs to be studied further.

The paper includes implications for the development of the knowledge management system. The proposed approach can be applied as a tool of knowledge sharing. It facilitates the finding of the knowledge that is related to the task at hand.

The paper provides new ways to find the relevant tasks and the related knowledge to the task at hand. Meanwhile, the new method to recommend the knowledge stage by stage is also proposed. It expands the research in the knowledge sharing and knowledge recommendation.

This paper aims to examine the way in which consumers integrate experts' opinions into their own evaluations of a selection of red wines.

The authors conduct an experiment to measure the influence of experts' opinions in relation to the complexity of the information, the level of consensus between different experts, and the consumer's level of category knowledge.

Confirmation is found for the effect of received information on consumers' product evaluations. Variation is found in relation to the consumer's level of category knowledge. Expert ratings have a stronger influence on individuals with low knowledge of the wine category than on those with high knowledge. The level of consensus between experts and the complexity of the information in this case have no effect on the impact of their opinions.

This paper takes a deeper look into the effect of “weak‐tie” personal information sources, particularly the opinions of experts regarding wine. Scientific research into the effect of expert judgments on consumer perceptions is still scant and businesses also need to assess the factors underlying its impact, given that the influence of expert judgment can be as crucial as quality to a product's success. The main feature that distinguishes this paper from the previous literature is that it integrates all three moderating effects in a single experiment: level of expert consensus, the complexity of the information provided and the prior knowledge of the consumer.

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

To build assembly machines to best suit the needs of anticipated production, machine builders need to spend much time deciding what type of machine system and control system to select and what kinds of devices to use in material handling. Especially when an expert builder leaves a company, knowledge goes with him and is lost to the company. Therefore, retention of valuable information in the form of a knowledge base is a very desirable resource. The purpose of this paper is to develop a computer‐based expert system that can be used as a consulting system by assembly machine builders to conquer the above difficulties. This expert system collects expert assembly machine knowledge and experience. Then, an evaluation is performed to ensure accuracy and reliability, and performance is evaluated by building a practical product. The expert system is found to be a valuable tool, providing significant information for machine builders.

From 2006 onwards very large Belgian nonprofit organizations (NPOs) are legally required to appoint an external auditor. In this context we investigate whether auditor choice in favor of a sector expert, being a higher quality auditor, is associated with NPOs’ expectations regarding several auditor attributes. We find that NPOs are more likely to choose a sector expert if they attach higher importance to an auditor’s client focus and relationship with management. NPOs are less likely to choose a sector expert if they care more about the practical execution of the audit. We provide recommendations for increasing the appeal of sector expertise as valuable auditor attribute. The resulting quality increase of NPOs’ financial statements and audit reports could benefit various stakeholders.