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Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.

First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.

Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.

A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.

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 study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge push algorithm was proposed.

Using a ratio of 80–20%, the experiment randomly splits the data into a training set and a test set. Each video is used as a knowledge unit (structure) in the research, and the category is used as a knowledge attribute. The limit is then determined using the user’s overall rating. To calculate the pertinent information obtained through experiments, the fusion coefficient is needed. The impact of the push model is then examined in comparison to the conventional push model. In the experiment, relevant knowledge is compared using three push models, two push models based on conventional International classification functioning (ICF), and three push models based on traditional ICF. The average push cost accuracy rate, recall rate and coverage rate are metrics used to assess the push effect.

The three-way knowledge push models perform better on average than the other push models in this research in terms of push cost, accuracy rate and recall rate. However, the three-way knowledge push models suggested in this study have a lower coverage rate than the two-way push model. So three-way knowledge push models condense the knowledge push and forfeit a particular coverage rate. As a result, improving knowledge results in higher accuracy rates and lower push costs.

This research has practical ramifications for the quick expansion of knowledge and its hegemonic status in value creation as the main methodology for knowledge services.

To the best of the authors’ knowledge, this is the first theory developed on the three-way decision-making process of knowledge push services to increase organizational effectiveness and efficiency.

Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge‐based recommendation model to provide good cold start recommendations.

Hybridizing a collaborative system and a knowledge‐based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers' preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach.

The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge‐based recommender systems improves the performance in those situations when collaborative models do not work properly.

Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations.

A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants.

Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.

The purpose of the paper is to conduct an exploratory study that proposes a personalized knowledge integration platform for digital libraries which can provide users with personalized information and knowledge services.

A prototype system (PIKIP_DL) is designed and developed with two types of service, i.e. personalized information/knowledge service and personalized subject category service. Evaluation of the PIKIP_DL by domain specialists and software experts is conducted. Comments are implications are addressed.

The main findings include the following: the proposed system can help suggest materials that readers are interested in for DL; the proposed system can help construct knowledge contents in a hierarchical structure; and a common recommendation concerning knowledge structure from the reviewers is that the proposed system should add a self‐organizing knowledge map function that would allow users to view knowledge subjects in a graphic manner.

The results from the evaluation of reviewers revealed that the proposed PIKIP_DL is acceptable to the integration of both personalized information service and personalized knowledge subject service. This implies that librarians and DL software agents should place emphasis on integrated service development to attract the attention of their users. Towards this goal, they could explain that personalized services (e.g. material recommendation, message recommendation, knowledge subject materials) with a mechanism of multi‐resource integration can help provide DL resources according to users' needs and wants, and in consequence to enhance DL service efficacy.

The research describes the importance of information/knowledge integration with respect to its support on the learning and study methods of users, and has developed a personalized knowledge integration platform as a mechanism that provides a personalized information service and a personalized knowledge subject category service. By employing Apriori algorithm and association rules as the data mining mechanism, personalized information recommendations are derived from circulation data, and a knowledge subject category is integrated from online sharing knowledge by participants.

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.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

The purpose of this paper is to explore the key factors that affect consumer redemption intention toward mobile coupons recommended in social network sites (SNS).

A research model that integrates recommendation trust, positive utilities, and negative utilities of coupon redemption is developed. With the important role of trust in social recommendation taken into consideration, the key drivers of recommendation trust were analyzed in the model. Data were collected from 210 users with mobile coupon recommendation experience in one of the largest SNS (i.e. WeChat) in China. The authors used partial least squares technique to analyze the model.

Recommendation trust and positive utilities (economic benefits and perceived enjoyment) positively affect the intention of mobile coupon redemption. Perceived risk, as a negative utility, negatively influences coupon redemption intention. In addition, swift trust (structure assurance, perceived similarity, trust propensity, and expertise of the recommender), knowledge-based trust (familiarity with the retailers), and emotion-based trust (social tie strength) are key drivers that promote recommendation trust.

While prior research investigated mobile coupon redemption behavior in which coupons were issued by merchants, limited research analyzed consumer responses toward mobile coupons in social recommendation. This study examines the effects of recommendation trust, positive utilities, and negative utilities on mobile coupon redemption in the context of social recommendation and recognizes the key drivers of recommendation trust.