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The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations. Our model incorporates a double-tower network (DT-Net) that learns user and item representations through a meta-encoder and a mutual attention encoder, effectively mitigating the impact of noisy data on auxiliary information. By framing the cold-start problem as few-shot meta-learning, we employ a model-agnostic meta-optimization strategy to train the model across a variety of tasks during the meta-learning phase. Extensive experiments conducted on three real-world datasets-ShortVideos, MovieLens, and Book-Crossing-demonstrate the superiority of our model in cold-start recommendation scenarios. Compared to MetaCs-DNN, the second-best approach, CFSM, achieves improvements of 1.55%, 1.34%, and 2.42% under the AUC metric on the three datasets, respectively.

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The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.

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INTRODUCTION: The aim of the paper is to establish the requirements and methodology for the development and implementation of a recommender system for mental health apps to support patients in self-managing their mental health while awaiting formal treatment. METHODS: The system was developed using an algorithm-based approach, including: (1) user needs assessment through literature review and interviews with various stakeholders, (2) software modelling and prototype creation, and (3) bench testing of the prototype with health experts and users. RESULTS: Based on initial exploration of users' requirements, relevant standards and regulations, a library of trusted mental health apps was compiled and a recommendation engine was built to generate accurate user profiles and deliver personalised health recommendations, which will be further tested to ensure quality. CONCLUSION: Developing a constructive mental health recommendation system requires the establishment of clear and comprehensive requirements, as well as a robust methodology adressing concerns related to data security, confidentiality, safety, and reliability. Subsequent research may compare various indicators of mental health outcomes at the start and end of patients' waiting period to gain more insights into how the recommender system could be further improved to enhance user experience and their overall well-being.

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Today, the number of published scientific articles is increasing day by day, and this has made the process of searching for articles more difficult. The need to provide specific recommender systems (RSs) for suggesting scientific articles is strongly felt in this situation. Because searching for articles based only on matching the titles or content of other articles is not an efficient process. In this research, the combination of two content analysis and citation network is used to design an RS for scientific articles (RECSA). In RECSA, natural language processing and deep learning techniques are used to process the titles and extract the content attributes of the articles. For this purpose, first, the titles of the articles are pre-processed, and by using the Term Frequency Inverse Document Frequency (TF-IDF) criterion, the importance of each word in the title is estimated. Then the dimensions of the obtained attributes are reduced by using a convolutional neural network (CNN). Then, by using the cosine similarity criterion, the content similarity matrix of the articles is calculated based on the attribute vectors. Also, the link prediction approach is used to analyze the connections of scientific articles' citation network. Finally, in the third step of RECSA, the two similarity matrices calculated in the previous steps are combined using an influence coefficient parameter to obtain the final similarity matrix, and the recommendation operation is based on the highest similarity value. The efficiency of RECSA has been evaluated from different aspects and the results have been compared with previous works. According to the results, utilizing the combination of TF-IDF and CNN for analyzing content-based features, leads to at least 0.32 % improvement in terms of precision compared to previous works. Also, by integrating citation and content-based data, the precision of first suggestion in RECSA would be 99.01 % which indicates the minimum improvement of 0.9 % compared to compared methods. The results show that by using RECSA, the recommendation can be done with higher accuracy and efficiency.

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Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

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BACKGROUND: Caring for people with dementia is perceived as one of the most challenging caring roles, so effective and practical support is essential. One such innovative approach to internet-based tailored health intervention is the use of recommender system. OBJECTIVE: This study develops a dementia care intelligent recommender system (DCIRS) that can provide personalized and timely care recommendations for caregivers when they encounter difficult various care problems in people with dementia. METHODS: The development process was divided into 3 stages. In stage 1, we complete the construction of the domain knowledge graph of dementia care. In stage 2, the established domain knowledge graph of dementia care was introduced into the recommendation model by the way of graph embedding to form a recommendation model composed of graph embedding module and recommendation module. In stage 3, on the basis of the application of knowledge graph and recommendation mode, DCIRS was developed, for practical use. In addition, DCIRS has been validated for accuracy for assessing the correctness of the profiling and recommendation, by enrolling 56 caregivers. RESULTS: The proposed DCIRS has functions of knowledge graph management and dementia care decision support. Experiments on 56 caregivers in single class recommendation task; the value of accuracy is equals to 98.92% and indicates the high capability of DCIRS. CONCLUSIONS: This study was a pioneering research to develop a more comprehensive DCIRS for caregivers of people with dementia. According to the evaluation results, our DCIRS showing high specificity and accuracy. This system can provide a novel perspective for patient-centered and needs-based support of caregivers of people with dementia.

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In the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations. By incorporating a counterfactual viewpoint, we aim to elucidate the causal influences of static long-term preferences on next-item selections and enhance the overall robustness of predictive models. We introduce a dual-tower architecture with a novel data augmentation process and a self-supervised training strategy, tailored to tackle inherent biases and unreliable correlations. Extensive experiments demonstrate the effectiveness of our approach, outperforming existing benchmarks and paving the way for more accurate and reliable session-based recommendations.

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Background: Rich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be applied to a decision support system application to generate data summaries for similar patients during the clinical encounter to assist physicians and patients in making evidence-based shared treatment decisions. Objective: A human-centered design (HCD) process was used to develop a HRS for treatment decision support in orthopaedic medicine, the Informatics Consult for Individualized Treatment (I-C-IT). We also evaluate the usability and utility of the system from the physician's perspective, focusing on elements of utility and shared decision-making in orthopaedic medicine. Methods: The HCD process for I-C-IT included 6 steps across three phases of analysis, design, and evaluation. A team of informaticians and comparative effectiveness researchers directly engaged with orthopaedic surgeon subject matter experts in a collaborative I-C-IT prototype design process. Ten orthopaedic surgeons participated in a mixed methods evaluation of the I-C-IT prototype that was produced. Results: The HCD process resulted in a prototype system, I-C-IT, with 14 data visualization elements and a set of design principles crucial for HRS for decision support. The overall standard system usability scale (SUS) score for the I-C-IT Webapp prototype was 88.75 indicating high usability. In addition, utility questions addressing shared decision-making found that 90% of orthopaedic surgeon respondents either strongly agreed or agreed that I-C-IT would help them make data informed decisions with their patients. Conclusion: The HCD process produced an HRS prototype that is capable of supporting orthopaedic surgeons and patients in their information needs during clinical encounters. Future research should focus on refining I-C-IT by incorporating patient feedback in future iterative cycles of system design and evaluation.

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The evaluation of performance using competencies within a structured framework holds significant importance across various professional domains, particularly in roles like project manager. Typically, this assessment process, overseen by senior evaluators, involves scoring competencies based on data gathered from interviews, completed forms, and evaluation programs. However, this task is tedious and time-consuming, and requires the expertise of qualified professionals. Moreover, it is compounded by the inconsistent scoring biases introduced by different evaluators. In this paper, we propose a novel approach to automatically predict competency scores, thereby facilitating the assessment of project managers' performance. Initially, we performed data fusion to compile a comprehensive dataset from various sources and modalities, including demographic data, profile-related data, and historical competency assessments. Subsequently, NLP techniques were used to pre-process text data. Finally, recommender systems were explored to predict competency scores. We compared four different recommender system approaches: content-based filtering, demographic filtering, collaborative filtering, and hybrid filtering. Using assessment data collected from 38 project managers, encompassing scores across 67 different competencies, we evaluated the performance of each approach. Notably, the content-based approach yielded promising results, achieving a precision rate of 81.03%. Furthermore, we addressed the challenge of cold-starting, which in our context involves predicting scores for either a new project manager lacking competency data or a newly introduced competency without historical records. Our analysis revealed that demographic filtering achieved an average precision of 54.05% when dealing with new project managers. In contrast, content-based filtering exhibited remarkable performance, achieving a precision of 85.79% in predicting scores for new competencies. These findings underscore the potential of recommender systems in competency assessment, thereby facilitating more effective performance evaluation process.

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Since the start of the 21st century, there has been a rapid development of internet technology, causing electronic computers and smartphones to become increasingly popular. The e-commerce industry also experiences quick development. However, the recommendation technology of e-commerce progresses slowly, hindering it from keeping up with the changing times. To enhance the efficiency and accuracy of e-commerce recommender systems, this research introduces an e-commerce recommender system that utilizes an enhanced K-means clustering algorithm to manage commodity information. This method combines the K-means algorithm with a genetic algorithm by encoding the genetic algorithm, setting the initial population, defining the fitness function, and configuring other parameters. The results of the test indicated that the K-mean clustering algorithm and fuzzy C-mean algorithm had a recommendation accuracy of 87.9 % and 84.8 % respectively under the test dataset. The highest recommendation accuracy was observed from the improved K-mean clustering algorithm, which was 91.1 %. The convergence rate of the improved K-mean clustering algorithm was faster by 44 % compared to the traditional K-mean clustering algorithm and 73 % quicker than the fuzzy C-mean algorithm. The study's findings demonstrate that the refined K-means clustering algorithm greatly enhances the recommendation proficiency and precision of the e-commerce recommendation system, in comparison to other comparable algorithms. This research can potentially advance the e-commerce industry and stimulate its growth.

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In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.

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The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.

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BACKGROUND: People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. MATERIALS AND METHODS: This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. RESULTS: Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. CONCLUSION: Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.

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BACKGROUND: Recommender systems help narrow down a large range of items to a smaller, personalized set. NarraGive is a first-in-field hybrid recommender system for mental health recovery narratives, recommending narratives based on their content and narrator characteristics (using content-based filtering) and on narratives beneficially impacting other similar users (using collaborative filtering). NarraGive is integrated into the Narrative Experiences Online (NEON) intervention, a web application providing access to the NEON Collection of recovery narratives. OBJECTIVE: This study aims to analyze the 3 recommender system algorithms used in NarraGive to inform future interventions using recommender systems for lived experience narratives. METHODS: Using a recently published framework for evaluating recommender systems to structure the analysis, we compared the content-based filtering algorithm and collaborative filtering algorithms by evaluating the accuracy (how close the predicted ratings are to the true ratings), precision (the proportion of the recommended narratives that are relevant), diversity (how diverse the recommended narratives are), coverage (the proportion of all available narratives that can be recommended), and unfairness (whether the algorithms produce less accurate predictions for disadvantaged participants) across gender and ethnicity. We used data from all participants in 2 parallel-group, waitlist control clinical trials of the NEON intervention (NEON trial: N=739; NEON for other [eg, nonpsychosis] mental health problems [NEON-O] trial: N=1023). Both trials included people with self-reported mental health problems who had and had not used statutory mental health services. In addition, NEON trial participants had experienced self-reported psychosis in the previous 5 years. Our evaluation used a database of Likert-scale narrative ratings provided by trial participants in response to validated narrative feedback questions. RESULTS: Participants from the NEON and NEON-O trials provided 2288 and 1896 narrative ratings, respectively. Each rated narrative had a median of 3 ratings and 2 ratings, respectively. For the NEON trial, the content-based filtering algorithm performed better for coverage; the collaborative filtering algorithms performed better for accuracy, diversity, and unfairness across both gender and ethnicity; and neither algorithm performed better for precision. For the NEON-O trial, the content-based filtering algorithm did not perform better on any metric; the collaborative filtering algorithms performed better on accuracy and unfairness across both gender and ethnicity; and neither algorithm performed better for precision, diversity, or coverage. CONCLUSIONS: Clinical population may be associated with recommender system performance. Recommender systems are susceptible to a wide range of undesirable biases. Approaches to mitigating these include providing enough initial data for the recommender system (to prevent overfitting), ensuring that items can be accessed outside the recommender system (to prevent a feedback loop between accessed items and recommended items), and encouraging participants to provide feedback on every narrative they interact with (to prevent participants from only providing feedback when they have strong opinions).

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Stroke is one of the leading causes of death worldwide. Previous studies have explored machine learning techniques for early detection of stroke patients using content-based recommendation systems. However, these models often struggle with timely detection of medications, which can be critical for patient management and decision-making regarding the prescription of new drugs. In this study, we developed a content-based recommendation model using three machine learning algorithms: Gaussian Mixture Model (GMM), Affinity Propagation (AP), and K-Nearest Neighbors (KNN), to aid Healthcare Professionals (HCP) in quickly detecting medications based on the symptoms of a patient with stroke. Our model focused on three classes of drugs: antihypertensive, anticoagulant, and fibrate. Each machine learning algorithm was used to accomplish specific tasks, thereby reducing the partial search space, computational cost, and accurately detecting a primary drug class without loss of precision and accuracy. Our proposed model, called CRGANNC (Clustering Recommendation Gaussian Affinity Nearest Neighbors Classifier), effectively addresses the sparsity and scalability issues faced by content-based recommendation models. The CRGANNC model dynamically partition clusters into sub-clusters with variable numbers based on the group, and can diagnose healthy, sick, and at-risk patients, and recommend drugs to the HCP. In addition to our analysis, we developed a semi-artificial dataset with new features such as weakness, dizziness, headache, nausea, and vomiting, using a pipeline. This dataset serves as a valuable resource for researchers in the sensitive domain of stroke, providing a starting point for building and testing models when real data is often restricted. Our work not only contributes to the development of predictive models for stroke but also establishes a framework for creating similar datasets in other sensitive domains, accelerating research efforts and improving patient care. Our experiments were conducted on our dataset consisting of 9691 patient records, with 1206 records for stroke attacks and 8485 healthy patients. The CRGANNC model achieved an average precision of 0.98, recall of 0.95 and F1-score of 0.96 across all three drugs classes. Furthermore, our model demonstrated significant improvement in computational efficiency compared to existing content-based recommendation models, reducing the processing time by 25.80% . This results indicate the effectiveness of our model in accurately detecting medications for stroke patients based on their symptoms.

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Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human-computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.

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The tourism industry has currently grown in various aspects, including the types of attractions, their quantity, and the number of tourist visits in various regions, contributing positively to both regional and global economies. Historical transactions are essential for developing recommender systems, utilizing techniques such as Collaborative Filtering and Demographic Filtering. TripAdvisor is a reputable website providing a wide range of accessible tourism information, including attractions, user profiles, and ratings. However, this unstructured raw data requires processing to create an adequate dataset for recommender systems. This study conducted a series of data processing steps on the raw data, including data restructuring, validation, content addition, integration with Google Maps, normalization, and modeling. This study successfully produced an original dataset comprising User Transaction, Item or Attraction, Attraction Type, Continent, Region, Country, City, and Visiting Mode. It also includes an entity relational model for tourism in Indonesia, particularly in Bali, Malang, and Yogyakarta regions, based on various global user experiences. This dataset is adequate and essential for developing various models of tourism recommender systems such as using Collaborative Filtering.

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Purpose/Significance To explore the research progress of health recommender system(HRS),so as to provide refer-ences for medical personnel to build HRS to help intelligent health care.Method/Process The application of common recommendation technology and HRS in the field of health care is summarized by literature research,and the research status and development direction of HRS is discussed.Result/Conclusion HRS has been applied in health service recommendation,diet recommendation,health behavior promotion,disease prognosis characteristics and health risk prediction,chronic disease management,mental health promotion and medi-cation recommendation,and the related research is conducive to the development of intelligent health care.

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Recommender systems (RSs) have become an integral part of the hiring process, be it via job advertisement ranking systems (job recommenders) for the potential employee or candidate ranking systems (candidate recommenders) for the employer. As seen in other domains, RSs are prone to harmful biases, unfair algorithmic behavior, and even discrimination in a legal sense. Some cases, such as salary equity in regards to gender (gender pay gap), stereotypical job perceptions along gendered lines, or biases toward other subgroups sharing specific characteristics in candidate recommenders, can have profound ethical and legal implications. In this survey, we discuss the current state of fairness research considering the fairness definitions (e.g., demographic parity and equal opportunity) used in recruitment-related RSs (RRSs). We investigate from a technical perspective the approaches to improve fairness, like synthetic data generation, adversarial training, protected subgroup distributional constraints, and post-hoc re-ranking. Thereafter, from a legal perspective, we contrast the fairness definitions and the effects of the aforementioned approaches with existing EU and US law requirements for employment and occupation, and second, we ascertain whether and to what extent EU and US law permits such approaches to improve fairness. We finally discuss the advances that RSs have made in terms of fairness in the recruitment domain, compare them with those made in other domains, and outline existing open challenges.

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Consumer segmentation is an electronic marketing practice that involves dividing consumers into groups with similar features to discover their preferences. In the business-to-customer (B2C) retailing industry, marketers explore big data to segment consumers based on various dimensions. However, among these dimensions, the motives of location and time of shopping have received relatively less attention. In this study, we use the recency, frequency, monetary, and tenure (RFMT) method to segment consumers into 10 groups based on their time and geographical features. To explore location, we investigate market distribution, revenue distribution, and consumer distribution. Geographical coordinates and peculiarities are estimated based on consumer density. Regarding time exploration, we evaluate the accuracy of product delivery and the timing of promotions. To pinpoint the target consumers, we display the main hotspots on the distribution heatmap. Furthermore, we identify the optimal time for purchase and the most densely populated locations of beneficial consumers. In addition, we evaluate product distribution to determine the most popular product categories. Based on the RFMT segmentation and product popularity, we have developed a product recommender system to assist marketers in attracting and engaging potential consumers. Through a case study using data from massive B2C retailing, we conclude that the proposed segmentation provides superior insights into consumer behavior and improves product recommendation performance.