Recommendations For Strategies Research Articles

Compensatory Swallowing Strategies Recommended in Oncology Practice: Practice Patterns and Relationship to Dynamic Imaging Grade of Swallowing Toxicity (DIGEST) Grades.

Compensatory swallow strategies are recommended to improve swallow safety and efficiency; however, there is limited evidence on use in specific populations or their relationship to swallow study results. We sought to describe/explore strategy recommendations in an oncology practice and their relationship to Dynamic Imaging Grade of Swallowing Toxicity (DIGEST) grades as a marker of clinical utility of the tool. This is a sub-study of a STARI-guided retrospective implementation evaluation at a single comprehensive cancer center. Electronic health record databases were queried to sample all modified barium swallow studies (MBS) for all adult oncology patients from 2016 to 2021, excluding total laryngectomy, leak studies and those with missing DIGEST grades. For patients with multiple MBS studies across the study period, one MBS was randomly selected to be included in the analytic sample. DIGEST grade, diet recommendation, oncologic details, and swallow strategy details were chart abstracted. Strategies and oral intake recommendations were classified from least to most restrictive. This study included 4570 patients representing diverse oncology populations (46% head and neck). DIGEST grades indicating at least mild dysphagia (grades ≥ 1) were reported in 2486 of MBS (54%). Strategies were recommended in 2028 MBS (44%). As DIGEST grade increased so did strategy utilization and complexity (Spearman's r (rS) = 0.76, p < 0.0001). This pattern was consistent for Safety (rS = 0.68) and Efficiency (rS = 0.73) grades (both p < 0.0001). Swallow strategies are frequently recommended in oncology populations. This is the first study to show a systematic link between DIGEST grade and MBS compensatory strategy recommendations, supporting clinical effectiveness of DIGEST as an evidence-based practice tool.

Harmonizing Cognitive and Psychosocial Needs in Cognitive Training: Lessons Learned from Piano Training in Persons Living with Mild Cognitive Impairment

ABSTRACT Objectives This qualitative study explored the potential of piano training as a holistic intervention to enhance both cognitive and emotional well-being. The study aims to detail the experiences of older adults living with mild cognitive impairment (MCI) in a piano training program and recommend strategies to boost engagement. Methods Fourteen individuals (seven older adults with MCI and their family members) participated in individual semi-structured interviews before and after a 12-week group-based piano training program. These interviews were recorded, transcribed, open-coded, and analyzed by conventional content analysis. Results Participants with MCI reported improvements in mood, cognitive function, and social interaction. The learning process elicited mixed emotions, with both enjoyment and frustration occurring during the learning phases. Skill mastery and positive social interactions were crucial for sustained engagement throughout the program, although group settings sometimes led to negative self-comparisons. Conclusions Findings highlighted the need to address both cognitive and emotional needs in interventions for MCI. Neglecting these aspects can exacerbate feelings of low self-esteem and other negative emotions. Clinical implications Recommendations for cognitive training programs include positive reinforcement, personalized support, mental preparation for challenges, and rapport building to enhance participant motivation and adherence.

Multi-relation graph contrastive learning with adaptive strategy for social recommendation

Multi-relation graph contrastive learning with adaptive strategy for social recommendation

Observed determinants of urban outdoor thermal exposure during hot summer and snowy winter periods in a humid continental climate

Many cities in the midlatitudes experience both extreme heat and cold, and pedestrians are exposed to thermal extremes that cause bodily stress. With growing urban populations, city design that contributes to mitigating summer heat while reducing winter cold exposure is increasingly important. Pedestrian thermal exposure depends on several microclimatic factors, including shortwave and longwave radiation absorption, which can be quantified by the mean radiant temperature (Tmrt). Limited research has been conducted on the radiative components of thermal exposure in hot, humid summers and cold, snowy winters. We gathered micrometeorological data from diverse urban sites and in multiple seasons in Guelph, Canada, using a mobile human-biometeorological weather station (MaRTy cart) that applies the six-directional method to determine Tmrt. Seasonal datasets were analysed and compared to examine the drivers of thermal exposure and recommend strategies for mitigating heat and cold stress. In summer, shade is the primary factor that reduces daytime heat exposure and it slightly increases nighttime heat exposure. Enhanced pervious ground cover is a secondary factor day and night. In winter, reduced shade alleviated daytime cold exposure, while snow cover provided daytime benefits from increased solar reflections and post-sunset penalties associated with reduced longwave radiation from low snow surface temperatures.

A Critical Analysis of AI-Driven Recommendation Algorithms in E-Commerce: Impacts, Opportunities and Challenges

This paper conducted a critical analysis and review of literature, spanning from 2019 to 2023, on how recommendation algorithms affected consumer behavior in e-commerce. The study explored the role of AI-driven recommendation algorithms in influencing consumer behavior and examined the impacts of various recommendations on e-commerce dynamics. By screening relevant articles from the Web of Science Core Collection database, with predefined keywords, the study applied visualization techniques, using the Bibliometrix and Biblioshiny tools within R Studio. The research investigated the different recommendation algorithms, documented in prior studies and their effects on consumer behavior in e-commerce. Our analysis uncovered significant insights into how these AI-driven recommendations shape consumer behavior, highlighting its influence on purchase decisions and product engagement. This study provides actionable insights for e-commerce businesses, to leverage recommendation algorithms, to boost consumer engagement and enhance sales effectiveness. By understanding the impacts of diverse recommendations, businesses can tailor their recommendation strategies to better align with consumer preferences. This paper could contribute to broader understanding of the role of artificial intelligence in consumer decision-making processes and offer valuable perspectives for future research in this field.

FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value

Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods.

Do personal recommendations need to be personalized? Investigating the relationships between student differences and educational recommendations

Educational recommender systems have been supporting personalized learning in various ways. However, less discussion is conducted about whether and how to personalize the strategies to generate recommendations based on student differences. In this study, we aim at investigating how students judge recommendations based on different strategies, and how these judgments relate to student characteristics. We conducted a large-scale questionnaire survey to measure students’ Big-Five personality traits, confidence in the subjects, and their judgments on six types of recommendations. The answers collected from 735 high school students in Japan indicate that students had different judgments across different recommendation strategies, but similarly for English and mathematics. Furthermore, the correlations between student characteristics and their judgments on recommendations were stronger if the subject to learn was inconsistent with the subject they preferred. The results provide insights on how to design educational recommendations that not only cater to students’ traits, but also help foster and enhance their traits for better learning.

Research on the recommendation strategy of dual-channel manufacturers for hybrid e-commerce platforms

IntroductionIn the context of hybrid e-commerce platforms with reselling mode and agency mode, this study considers the issue of channel management by manufacturers through recommendation strategies.MethodsFor three dual-channel structures composed of e-commerce platforms, manufacturers, and third-party retailers, game models were constructed for manufacturer’s non-recommendation, differentiated recommendation, and indiscriminate recommendation scenarios to investigate the optimal recommendation strategy for manufacturers.Conclusion(1) For different dual-channel structures, compared to scenarios without recommendations, it is not always profitable for manufacturers to adopt a recommendation strategy as recommended parties may not necessarily gain higher profits from recommendations. (2) The optimal recommendation strategy for manufacturers is influenced by channel structure, commission rates, and relative scale in the recommended market. Recommending direct sales channels is the preferred choice for manufacturers with a higher relative scale in the recommended market prompting them to recommend all channels to consumers. (3) Numerical simulations reveal that retail prices, total market demand, and supply chain profits are positively correlated with relative scale within the recommended market. Additionally, any recommendation strategy can increase demand for recommended parties as well as overall supply chain profit levels.

Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations

The movie recommender system is one of the most influential and practical tools for aiding individuals in quickly selecting films to watch. Despite numerous academic efforts to employ recommender systems for various purposes, such as movie-watching and book-buying, many studies have overlooked user-specific movie recommendations. This paper introduces a novel approach for movie recommendations that combines the hesitant fuzzy clustering with a convolutional spiking neural network movie recommender system. The initial step involves acquiring input data from benchmark datasets like MovieLens 100K and MovieLens 1M. Further, content-based features are extracted from the dataset using ternary pattern and discrete wavelet transforms. After that hesitant fuzzy linguistic Bi-objective clustering (HFLBC) is applied for cluster selection based on the extracted features. Subsequently, a movie recommender scheme utilizing a convolutional spiking neural network is introduced to predict user film preferences. The efficiency of the proposed model is compared to existing methods such as multi-modal trust-dependent recommender scheme and graph-dependent hybrid recommendation scheme. The results show a significant improvement, with the proposed model achieving 13.79% and 16.47% higher accuracy than the existing methods. The findings highlight the potential of proposed system in enhancing the accuracy and personalization of movie recommendations.

What do you need to know when preparing to give a talk at an international dermatology conference? Insights and practical recommendations.

To provide a checklist for presentation preparation at dermatology conferences, discuss important factors to consider when preparing a presentation, and recommend strategies for effective presentations and networking. With a combination of personal experience and literature review of PubMed database and dermatology society resources, this article serves as the first comprehensive guide for how to prepare a talk for an international dermatology conference. Conferences are an excellent opportunity to learn more about yourself, your field, and others throughout the world. Well-prepared presentations have the potential to greatly impact your audience and expand your connections. The authors provide a step-by-step discussion and checklist that thoroughly addresses the logistics, operations, scientific content, and social aspects that are important to know when preparing to give a presentation in the field of dermatology.

POI Recommendation Scheme Based on User Activity Patterns and Category Similarity

The utilization of location-based social networks to provide point-of-interest (POI) recommendation services has been the subject of extensive research in recent years. Various factors that can enhance the precision of POI recommendations were examined in previous studies. However, the factors of a user, including the location and time, were not considered. In this paper, we proposed a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. The proposed scheme is used to organize users based on the activity level and to take into account the characteristics of both the user and location. Furthermore, it provides personalized recommendations by considering the category similarity, time, and location data that were collected from users. We evaluated the performance of the proposed scheme and compared it with that of a currently used scheme. The proposed scheme exhibits precision that is approximately 16% greater than that of the existing scheme.

Research on Innovation and CSR of Small and Medium Textile Machinery Enterprises

This study focuses on small and medium-sized textile machinery enterprises in China, exploring how they can optimize sales performance through innovation and corporate social responsibility (CSR) practices, combined with organic online traffic and word-of-mouth recommendation strategies. Amidst the backdrop of fierce global competition in the textile industry, shifting consumer preferences, and heightened environmental requirements, the COVID-19 pandemic and Sino-American trade conflict have further exacerbated challenges faced by the industry. As a crucial link in the textile supply chain, textile machinery enterprises have also been impacted. Despite these difficulties, small and medium-sized textile machinery enterprises play a significant role in industry development and competitiveness. By integrating modern marketing strategies with CSR practices, this study aims to reveal how enterprises can effectively communicate their CSR commitments to expand market influence, boost sales growth, and strengthen brand reputation. The findings indicate that innovation and CSR play a pivotal role in enhancing corporate competitiveness, differentiating from competitors, and fostering brand loyalty. Based on interviews with a family-owned enterprise, this study observes that while the enterprise has achieved notable success in CSR practices, there is room for improvement in its marketing and communication strategies. Therefore, this paper proposes improvements such as optimizing the official website to highlight CSR, increasing investment in social media marketing, and combining CSR with offline word-of-mouth recommendations, with the aim of helping enterprises more effectively convey their CSR philosophies, attract potential customers, and drive sustained business growth.

Review-Based Explainable Recommendations: A Transparency Perspective

With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the deep models’ input drive the final ranking decision, thus, they cannot often be understood by human stakeholders. In this paper, we investigate the dilemma between recommendation and explainability, and show that by utilizing the contextual features (e.g., item reviews from users), we can design a series of explainable recommender systems without sacrificing their performance. In particular, we propose three types of explainable recommendation strategies with gradual changes in model transparency: whitebox, graybox, and blackbox. Each strategy explains its ranking decisions via different mechanisms: attention weights, adversarial perturbations, and counterfactual perturbations. We apply these explainable models on five real-world data sets under the contextualized setting where the users provided explicit reviews of the items. The empirical results show that our models can achieve highly competitive ranking performance, and meanwhile, generate accurate and effective explanations in terms of numerous quantitative metrics and qualitative visualizations.

Perioperative patient safety recommendations: systematic review of clinical practice guidelines.

Surgical-related incidents are a common cause of in-hospital adverse events. Surgical patient safety would benefit from evidence-based practices, but a comprehensive collection of patient safety recommendations is still lacking. This study aimed to compile and assess the perioperative patient safety recommendations for adults. A systematic review of clinical practice guidelines was conducted using Medline, Embase, Cochrane, Virtual Health Library Regional Portal, and Trip Database from 2012 to 2022. Eligibility criteria followed a PICAR strategy for patient safety recommendations in the perioperative care continuum. Guidelines were appraised for quality, particularly focusing on the 'rigour of development' domain of the AGREE-II tool for those containing strong recommendations. Descriptive analyses were conducted, emphasizing guideline quality, recommendation strength, and the supporting level of evidence. From the 267 guidelines, 4666 perioperative patient safety recommendations were extracted, of which 44.9% (2095) were strongly recommended. Of these, 322 had the highest level of evidence, but only 18 guidelines met high standards in the AGREE-II 'rigour of development' domain. A subset of 78 recommendations ranked the highest in the strength of recommendation, level of evidence, and rigour of development of their guidelines. A gap was found within pre-admission and post-discharge care recommendations. This review highlights the noteworthy variability in the methodological quality of the guidelines, and a discordance between strength of recommendation and evidence level of the available perioperative patient safety recommendations. These findings provide valuable information for advising policy decisions and promoting best practices to enhance global surgical safety. PROSPERO (CRD42022347449).

JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced Transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability.

Empowering Retail Dual Transformer-Based Profound Product Recommendation Using Multi-Model Review

Advancements in technology have significantly changed how we interact on social media platforms, where reviews and comments heavily influence consumer decisions. Traditionally, opinion mining has focused on textual data, overlooking the valuable insights present in customer-uploaded images—a concept we term Multus-Medium. This paper introduces a multimodal strategy for product recommendations that utilizes both text and image data. The proposed approach involves data collection, preprocessing, and sentiment analysis using Vti for images and SpanBERT for text reviews. These outputs are then fused to generate a final recommendation. The proposed model demonstrates superior performance, achieving 91.55% accuracy on the Amazon dataset and 90.89% on the Kaggle dataset. These compelling findings underscore the potential of our approach, offering a comprehensive and precise method for opinion mining in the era of social media-driven product reviews, ultimately aiding consumers in making informed purchasing decisions.

Leveraging recommendations using a multiplex graph database

PurposeBy applying targeted graph algorithms, the method used by the authors enables effective prediction of user interactions and thus fulfils the complex requirements of modern recommender systems. This study sets a new benchmark for multidimensional recommendation strategies and offers a path towards more advanced and user-centric models.Design/methodology/approachTo improve multidimensional data recommendation systems, multiplex graph structures are useful to capture various types of user interactions. This paper presents a novel framework that uses a graph database to compute and manipulate multiplex graphs. The approach enables flexible dimension management and increases expressive power through a specialised algebra designed for multiplex graph manipulation.FindingsThe authors compare the multiplex graph approach with traditional matrix methods, in particular random walk with restart, and show that the method not only provides deeper insights into user preferences by integrating scores from different layers of the multiplex graph, but also outperforming matrix-based approaches in most configurations. The results highlight the potential of multiplex graphs for developing sophisticated and customised recommender systems that significantly improve both performance and explainability.Originality/valueThe study provides a formal specification of a multiplex graph construction based on interaction and content-based information; and the study also developed an algebra dedicated to multiplex graphs, enabling robust and precise graph manipulations necessary for effective recommendation queries. The authors implement these algebraic operations within the Neo4j graph database system with a thorough analysis and experimentation with three different data sets, benchmarked against traditional matrix-based methods.

The effect of the product categories diversity recommended on cross‐buying in electronic commerce platforms: The moderating role of user navigation heterogeneity

AbstractE‐commerce platforms encourage consumers' cross‐buying behavior to boost user traffic and trading volume. However, balancing the precision and richness of recommended information with the mechanisms influencing consumer cross‐buying remains unclear, which also poses challenges to the specific operation of these platforms. Therefore, this study leverages extensive online behavioral data from an e‐commerce platform, encompassing detailed records of 26,034 consumers. By employing unsupervised machine learning algorithms to distinguish the heterogeneity of consumer browsing, we apply clickstream data to explore the mechanisms of the impact of information diversity, specifically the breadth and depth of recommended product categories, on cross‐buying. This study reveals that the effect of diverse recommendations on online decision‐making undergoes marginal variations due to alterations in both the breadth and depth of these recommendations. Specifically, the depth and breadth of recommended product categories exhibit an inverted U‐shaped relationship with cross‐buying, initially promoting, and subsequently suppressing it. Additionally, consumers with different browsing behavior characteristics respond differently to recommendation diversity. Confused visitors and hedonic visitors positively moderate the depth of information and negatively moderate the breadth of recommendations with the inverted U‐shaped effect on cross‐buying. Search‐oriented visitors positively adjust the inverted U‐shaped relationship between the depth of information and the breadth of recommendations on consumer cross‐buying. This study enriches research on cross‐buying in the domain of recommendations on e‐commerce platforms from the perspective of information diversity, providing valuable insights for optimizing recommendation strategies, promoting cross‐buying behavior, and enhancing consumer loyalty.

Attitudes, healthcare interactions, and communication preferences for HPV vaccines among hesitant Hispanic/Latinx parents: how does this compare with influenza and COVID-19 vaccines?

ABSTRACT Objective We explored HPV vaccine concerns and healthcare provider communication among Hispanic/Latinx hesitant parents during the COVID-19 pandemic, and how those concerns and provider communications compared for influenza and COVID-19 vaccines. As a secondary aim, we explored communication needs and strategies for these recommended vaccines. Design We applied a concurrent, multi-method study design (survey and interview) with 23 Hispanic/Latinx American parents of adolescents aged 11–18 years. An inductive deductive thematic approach was used to interpret interview data. Descriptives were used to analyze survey data. Results Overall, most parents (77%) perceived that their child was receiving too many vaccines. Regarding hesitancy for the HPV vaccine, major parental concerns related to vaccine effectiveness (83%), vaccine necessity (83%), and vaccine safety (80%). For the COVID vaccine, major concerns were the newness of vaccine (83%), vaccine safety (77%), low perceived effectiveness (77%), and low perceived need (77%). For influenza vaccines, major parental concerns were vaccine safety (73%), low perceived need (73%), and preference for natural over vaccine-induced immunity (73%). Parents had overall positive views towards vaccination. Some parents prioritized school-required vaccines, and others ranked the recommended vaccines- HPV, COVID-19, and influenza vaccines- for their children. They saw the benefits of recommended vaccines; however, there were concerns. The majority received a provider’s recommendation for HPV vaccination for their child, while over half had not received a recommendation for COVID-19 and influenza vaccines. Recommended communication strategies were diverse with parents highlighting the need for providers to capitalize on waiting and clinic rooms to provide education. Conclusion Healthcare providers should take advantage of missed opportunities, improve healthcare interactions with a strong recommendation and persistent communication, and offer diverse communication strategies and messaging for vaccines. Future work should further explore multi-vaccine concerns and how addressing these concerns through healthcare-provider communication could improve HPV vaccination along with COVID-19 and influenza vaccination.

Implementation Of Indonesia's Defence Economy Through The Defence Budget Sector

The implementation of military spending through the Indonesian defense industry has shown gradual positive development, driven by efforts to establish an independent defense sector. This study examines the role of Indonesia's defense budget in supporting national security and fostering economic growth, focusing on the collaboration between state-owned enterprises (BUMN) and private companies (BUMS). The defense budget is applied progressively to balance the country's limited resources and the relatively low level of external threats. Using a qualitative research method, this study analyzes data from relevant literature, reports, and policies to evaluate the impact of the defense budget on the Indonesian economy. The research explores key aspects such as the gradual development of defense equipment, the role of research and development (R&amp;D) in driving innovation, and the partnership between public and private sectors to strengthen the local defense industry. The results indicate that Indonesia's defense budget has steadily increased over the past decade, contributing to economic growth, particularly in the manufacturing and technology sectors. However, the overall impact on the economy remains mixed due to challenges such as bureaucratic inefficiencies and dependence on imported materials. The discussion highlights the potential for defense R&amp;D investments to generate spillover effects into civilian industries and recommends strategies to enhance the efficiency of defense spending. In conclusion, the implementation of defense economic policies has laid a strong foundation for the development of an independent defense industry in Indonesia. The study suggests that a gradual increase in defense spending, combined with strategic partnerships and innovation, can support both national security and economic stability in the long term