User Interest Research Articles

Exploration of UCD Practice Limitations

Abstract The integration of user-centred design (UCD) practices in software development is fundamental for enhancing the overall experiences of software users. In this paper, we summarize the results of various research studies on what the stated limitations of UCD practices are. A process called the UCD Sprint was introduced recently with the goal of addressing some of the existing UCD practice limitations for integrating UCD practices in software development. The guided usage of the step-by-step process has been studied with students and UCD practitioners in courses showing positive results, where participants describe benefits while using the process, such as the speed of the process, its clear structure, guidance. In this paper, we describe results from a synthesis study summarizing results on how guided usage of the UCD Sprint can address UCD practice limitations. Furthermore, we describe results from a series of interviews with UCD practitioners on how they currently practice UCD, the challenges and limitations they experience and their views on how unguided usage of the UCD Sprint would potentially fit their UCD practices. While the interviewees recognize the importance of user involvement, they highlight challenges in aligning user interests with other stakeholders' interests. All participants had used processes like design sprints for structuring their UCD practices. Interviewees have generally tailored the traditional approach of design sprints, showcasing a nuanced understanding of UCD practices. The results show that the UCD Sprint process is considered by UCD practitioners as a promising approach for practicing UCD. The process is viewed with enthusiasm, but the need for clear objectives and adaptability to the work contexts of the UCD practitioners remains essential. UCD practitioners’ adaptive strategies of their UCD practices emphasize the need for flexibility and adaptation of UCD practices and processes.

SYRMEP beamline: state of the art, upgrades and future prospects

SYRMEP is the hard X-ray imaging beamline of Elettra synchrotron offering X-ray full-field techniques, micro-computed tomography (microCT) and phase-contrast modality in the energy range 10–40 keV. The beamline operates in a multidisciplinary research context spanning from biomedical applications to botany, from zoology to food technology and cultural heritage, from materials engineering to geology and earth science. Thanks to the flexibility of SYRMEP setup, in situ experiments can be performed as well, novel imaging methods can be developed and implemented in a synergical manner with interested users and collaborators. SYRMEP peculiar wide beam together with the long sample-to-detector distance enables multiscale phase-contrast studies with optimized contrast and spatial resolution on rather large specimens, such as human lung phantoms. This is particularly relevant in view of future clinical lung imaging foreseen in the framework of Elettra 2.0 program. Here, the current beamline features and recent upgrades are illustrated, an overview of the imaging methods routinely offered to SYRMEP users’ community is presented, and the outlook for the new beamline SYRMEP-Life Science (SYRMEP-LS) is reported.

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

Quantum Representation based Preference Evolution Network for E-commerce recommendation

Quantum theory, originally developed to explain microscopic physical systems, has recently emerged as a novel conceptual and mathematical framework in information science. This paper applies quantum theory to address challenges in E-commerce recommendation, specifically those involving sequential behavior, aiming to mine effective patterns of preference evolution and more accurately predict user interests. Current recommender systems are limited by the sequence length and underutilize side information such as item attributes. To address these issues, we propose a Quantum Representation-based Preference Evolution Network (QRPEN) for E-commerce recommendations. Unlike traditional methods that focus solely on item-ID, our approach integrates a comprehensive set of side information, including both item-ID and attribute data, at each timestamp. We represent item attributes using quantum superposition states and employ density matrices to describe the probability distribution of same-type attributes. These matrices are then transformed into vectors through a quantum measurement-inspired process and fed into a Quasi-RNN model, enabling parallelization and the modeling of longer sequences. This approach effectively captures the dynamic evolution of user preferences. Experiments on public E-commerce datasets demonstrate that QRPEN achieves competitive performance.

Interest HD: An Interest Frame Model for Recommendation Based on HD Image Generation.

This work is inspired by high-definition (HD) image generation techniques. When the user's interests are viewed as different frames of varying clarity, the unclear parts of one interest frame can be clarified by other interest frames. The user's overall HD interest portrait can be viewed as a fusion of multiple interest frames through detail compensation. Based on this inspiration, we propose a model for generating HD interest portrait called interest frame for recommendation (IF4Rec). First, we present a fine-grained pixel-level user interest mining method, Pixel embedding (PE) uses positional coding techniques to mine atomic-level interest pixel matrices in multiple dimensions, such as time, space, and frequency. Then, using an atomic-level interest pixel matrix, we propose Item2Frame to generate several interest frames for a user. The similarity score of each item is calculated to fill the multi-interest pixel clusters, through an improved self-attention mechanism. Finally, stimulated by HD image generation techniques, we initially present an interest frame noise compensation method. By utilizing the multihead attention mechanism, pixel-level optimization and noise complementation are performed between multi-interest frames, and an HD interest portrait is achieved. Experiments show that our model mines users' interests well. On five publicly available datasets, our model outperforms the baselines.

A stackelberg game-based programming approach for industrial steam systems incorporating renewable energy considering demand response

Fossil fuels account for 37 % of industrial combustion, primarily used for steam production. Given the significant role of steam in industrial processes, industrial steam systems (ISS) offer a critical opportunity to accelerate the transition from fossil fuels to renewable energy by integrating renewable sources. Currently, ISS optimization focuses mainly on internal improvements, often neglecting stakeholder interactions and the inherent unpredictability of renewable energy sources. This paper introduces a novel bi-level programming approach based on Stackelberg game theory to address the balance between ISS and user interests. A new ISS model is proposed that integrates multiple renewable energy sources while accounting for their intermittency. Revenue models for energy servers (ES) and consumers (EC) are developed, and demand response mechanisms and real-time pricing are incorporated into the bi-level programming model. In the designed bi-level ISS (B-L-ISS) model, the upper level optimizes energy conversion equipment and pricing, while the lower level aims to minimize users' energy costs. The Karush-Kuhn-Tucker (KKT) conditions are used to reformulate the bi-level problem into a single-level programming problem with equilibrium constraints. Validation on a real coastal industrial steam system shows that the proposed method integrates renewable energy four times more effectively than traditional methods, reducing CO2 emissions by 15,790 tons annually, while balancing the interests of both the steam system and its users.

Interest-aware joint caching, computing, and communication optimization for mobile VR delivery in MEC networks

In the upcoming B5G/6G era, Virtual Reality (VR) over wireless has become a typical application, which is an inevitable trend in the development of video. However, in immersive and interactive VR experiences, VR services typically exhibit high delay, while simultaneously posing challenges for the energy consumption of local devices. To address these issues, this paper aims to improve the performance of VR service in the edge-terminal cooperative system. Specifically, we formulate a joint Caching, Computing, and Communication (3C) VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices. To design the optimal VR service policy, the optimization problem is decoupled into three independent subproblems to be solved separately. To improve the caching efficiency within the network, a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior. Based on this, a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users. Then, the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy. With alternative optimization, an optimal policy for joint 3C based on user interest can be finally obtained. Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness. Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.

Enhancing Latent Tuberculosis Infection Treatment Adherence with Mobile Health Intervention: A Quasi-Experimental Study

Introduction: While detecting active TB is central to public health efforts, modelling indicates that reducing latent TB through preventive therapy is crucial. Current regimens mitigate risk but are lengthy and have side effects, necessitating support for uninterrupted treatment. This paper presents the development and evaluation of a digital health platform designed to enhance adherence among LTBI patients. Methods: A Quasi-experimental study was conducted among LTBI patients in Delhi. A total of 163 participants were allocated to intervention (n=82) and control (n=81) groups. Participants were followed up for 6 months post recruitment. Effectiveness of mobile application was evaluated through quantitative tools. Results: Intervention group participants showed slightly higher treatment completion rates (65.91%), in comparison to participants in control group (63%). The analysis demonstrated no co-relation of gender, age, education and employment with treatment completion rates in intervention group. While text and video-based interventions have shown success, there remains a need for more user-centric digital health interventions in this area, given the limited number of studies to date. Conclusion: The mobile health applications can be useful for LTBI care. However, there is a need of involving users during development so that continued interest of users can be ensured.

A novel method for a technology enhanced learning recommender system considering changing user interest based on neural collaborative filtering

This study introduces an advanced recommender system for technology enhanced learning (TEL) that synergizes neural collaborative filtering, sentiment analysis, and an adaptive learning rate to address the limitations of traditional TEL systems. Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users. Utilizing Bidirectional Encoder Representations from Transformers for sentiment analysis, our system not only understands but also respects user privacy by processing feedback without revealing sensitive information. The adaptive learning rate, inspired by AdaGrad, allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback, ensuring a dynamic response to both positive and negative sentiments. This dual approach enhances the system’s adaptability to changing user preferences and improves its contentment understanding. Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners, facilitating a more personalized learning experience. By dynamically adjusting recommendations based on real-time user data and behavioral analysis, our system leverages the collective insights of similar users and relevant content. We validated our approach against three datasets—MovieLens, Amazon, and a proprietary TEL dataset—and saw significant improvements in recommendation precision, F-score, and mean absolute error. The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems, marking a step forward in developing more responsive and user-centric educational technologies. This study paves the way for future advancements in TEL systems, emphasizing the importance of emotional intelligence and adaptability in enhancing the learning experience.

CGG: Category-aware global graph contrastive learning for session-based recommendation

With the auxiliary role of category information in capturing user interests, employing category information to improve session-based recommendation (SBR) is getting an energetic research point. Recent studies organized the category-aware session as the graph structure and utilized the graph neural network to explore the session interest for SBR. However, existing studies only focused on the category information in the current session and failed to overcome inherent sparsity of session data, which resulted in suboptimal SBR performance. To overcome these deficiencies, we propose a Category-aware Global Graph contrastive learning method, namely CGG, for SBR. To be specific, we firstly construct the category-aware global graph based on global item-item transitions, item-category associations and global category-category transitions, which utilizes more sufficient category information across sessions to learn embeddings of categories and items. Furthermore, we design the hierarchical dual-pattern contrastive learning mechanism to model the information interaction of graphical and sequential patterns of a category-aware session, which overcomes the negative influence of sparse session data by injecting self-supervised signals. Extensive experiments on multiple real-world datasets verify that CGG outperforms seven mainstream SBR methods on different measurements.

CYBERCRIME CHALLENGES: EXPERIENCE OF INTERNATIONAL COOPERATION

The study focuses on the problem of countering cybercrime in the context of the experience of international cooperation. The trend in the process of ongoing transformation from a post-industrial to an information society stands out: global informatization and digitalization of processes lead to the threat of interference in personal life, data theft, and violation of privacy. The analysis of international legal acts made it possible to conclude the need to expand cooperation between states in the fight against cybercrime under the auspices of the UN based on programmatic, strategic acts of international legal regulation of the relevant range of relations. The importance of creating and developing organizational and legislative measures to combat cybercrime, improving interaction between the competent authorities of various states, as well as the need to develop and adopt universal conventions to combat cybercrime, is noted. The authors of the article state that the large-scale growth in the volume of information transfer causes the need to ensure the maximum level of data protection, both by the interested ICT users themselves and by governments - guarantors of state information security and stability. The international legal framework provides a solution to the problem in three areas: reducing discrepancies between national laws, introducing new powers of bodies, and promoting international cooperation. It is stated that in the Republic of Kazakhstan, there are no comprehensive studies on combating cyberterrorism and cybercrime as phenomena that include the entire range of crimes that are committed today on the Internet. The study fills this gap: the activities of the legislative and law enforcement bodies of the Republic of Kazakhstan are analyzed. The authors focus on such positive aspects as the creation of the Ministry of Internal Affairs of the Republic of Kazakhstan in April 2003 of the "K" department to intensify the fight against a wide range of crimes related to computer and Internet technologies, including cyberbullying, the production of counterfeit video discs, the dissemination of information that contributes to extremism, terrorism, cruelty, and violence. Chapter 7 of the Criminal Code of the Republic of Kazakhstan, devoted to criminal offenses in the field of informatization and communications, was subjected to scientific turnover; Cybersecurity Concept ("Cyber Shield of Kazakhstan"); a pilot project of 2023, which launched a special cyber police unit "Cyberpol Strike Task Force" to protect citizens and businesses of the country from cyber threats.

Методы корпусного анализа частотности и динамики языковых единиц в 2024 году

The article “Methods of corpus analysis of the frequency and dynamics of language units in 2024” presents possible methods and sources of searching, frequency analysis and diachrony (microdiachrony, nanodiachrony) of Russian language units using not only well-known linguistic corpora such as RNC and GICR, but also “natural corpora” of Yandex (including Yandex. News) and Google, as well as other search sources, such as LiveJournal, VKontakte and Telegram. The author indicates the possibility of using the official English-language corpora in the study of loanwords-neologisms in the modern Russian language. There is a discussion of the pros and cons of each service with special attention to the search tools that allow a linguist to specify the data. In the article there also are considered search services in the databases of digitized books: in Google Books (including the Google Books Ngram Viewer) and the electronic catalog of the Russian State Library. There is a presentation of the services (Yandex. Wordstat and Google Trends) that show user interest in a language unit – a word or phrase. All or almost all of the services, the possibility of using which is analyzed in the article, have already been used in one way or another in linguistic analysis in scientific papers. However, it should be considered that, firstly, it is often necessary to use several sources at once for an adequate analysis, and, secondly, the choice of priority sources depends on the type of the information a linguist is looking for. Conclusions are drawn about the possible order of use of the services discussed for different categories of words (long-known; words of the Runet era; very new and rare words; words for which it is necessary to find out the frequency by half-year or by month) and the need to be able to use each of these tools is justified.

Hypergraph contrastive learning for recommendation with side information

PurposeThis paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.Design/methodology/approachThe MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.FindingsExtensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.Originality/valueThe novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

Development of Boxing Sports Samsak for Novice Athletes

The purpose of the study. This study aims to determine the feasibility of developing laseaban bags in boxing. Materials and methods. The research sample was 30 athletes. The feasibility of the bag is first validated by experts, namely equipment experts, material experts (boxing coaches), and sports experts. Furthermore, small group tests and large group tests are tested on athletes. Results. In a large group test with a total of 20 (twenty) athletes with 3 (aspects) with an indicator of the suitability of the bag with the comfort of using the bag, an indicator of implementation, and an indicator of user interest with 10 (ten) questions. With an average score of 4.35 with a percentage of 97.00% in the very good category. Conclusions. The following is a response to the interest of athletes when using or using a tool developed by the researcher, namely the laseaban bag.

TCSR: Self‐attention with time and category for session‐based recommendation

AbstractSession‐based recommendation that uses sequence of items clicked by anonymous users to make recommendations has drawn the attention of many researchers, and a lot of approaches have been proposed. However, there are still problems that have not been well addressed: (1) Time information is either ignored or exploited with a fixed time span and granularity, which fails to understand the personalized interest transfer pattern of users with different clicking speeds; (2) Category information is either omitted or considered independent of the items, which defies the fact that the relationships between categories and items are helpful for the recommendation. To solve these problems, we propose a new session‐based recommendation method, TCSR (self‐attention with time and category for session‐based recommendation). TCSR uses a non‐linear normalized time embedding to perceive user interest transfer patterns on variable granularity and employs a heterogeneous SAN to make full use of both items and categories. Moreover, a cross‐recommendation unit is adapted to adjust recommendations on the item and category sides. Extensive experiments on four real datasets show that TCSR significantly outperforms state‐of‐the‐art approaches.

What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamics

Virtual 3D assets, i.e., 3D models that form the basis of virtual environments, products, and goods, are essential for the creation of a future metaverse. However, we have limited knowledge about the market dynamics in which virtual 3D assets are traded and the indicators that influence their value and pricing − and thus the purchasing mechanisms. The present study draws on multi-attribute utility and value- and competition-informed pricing theory to determine what drives the purchase of virtual 3D assets using secondary data from the marketplace Sketchfab. The empirical analysis indicates that the sellers’ value perception of virtual 3D assets contradicts the users’ interest and that organizational sellers outperform individual sellers by relying to a higher degree on competition- and value-informed pricing. Based on our findings, we identify implications for both organizational and individual sellers to refine their pricing strategies in accordance with the unique dynamics of 3D virtual asset marketplaces.

Evaluating the Economic Viability of Agro-Ecotourism as a Nature-Based Solution for a Climate Adaptation Strategy: A Case Study of Yuanshan Township, Taiwan

This study applied the contingent valuation method to evaluate the economic viability of climate adaptation policies in the climate-fragile Yuanshan Township (YST), Taiwan, focusing on the balance between forest conservation and local livelihoods. Traditional agriculture in YST is transitioning to agro-ecotourism, supported by leisure-oriented and hillside forest protection policies that attract visitors and cause the local economy to thrive. This research used non-market valuation methods to quantify the value of local recreational resources perceived by visitors at NTD 1002.00 per visitor. The findings indicate that visitors’ willingness to pay for these resources is significantly influenced by their intention to revisit YST, their trust in local conservation efforts, their gender, and their income. The value that visitors place on recreational resources and the aforementioned significant determinants of their willingness to pay highlight the economic potential of agro-ecotourism in supporting both environmental sustainability and community income. This study emphasizes that successful nature-based climate adaptation must consider the economic interests of resource users and local residents. Agro-ecotourism in YST is shown to be a viable policy for balancing forest conservation with societal benefits, providing a model for the sustainable management of local resources. The economic benefits from this transition underscore the feasibility of agricultural transformation for community income generation and climate resilience, demonstrating that environmental and economic goals can be mutually supportive in addressing climate change.

HyperCLR: A Personalized Sequential Recommendation Algorithm Based on Hypergraph and Contrastive Learning

Sequential recommendations aim to predict users’ next interactions by modeling their interaction sequences. Most existing work concentrates on user preferences within these sequences, overlooking the complex item relationships across sequences. Additionally, these studies often fail to address the diversity of user interests, thus not capturing their varied latent preferences effectively. To tackle these problems, this paper develops a novel recommendation algorithm based on hypergraphs and contrastive learning named HyperCLR. It dynamically incorporates the time and location embeddings of items to model high-order relationships in user preferences. Moreover, we developed a graph construction approach named IFDG, which utilizes global item visit frequencies and spatial distances to discern item relevancy. By sampling subgraphs from IFDG, HyperCLR can align the representations of identical interaction sequences closely while distinguishing them from the broader global context on IFDG. This approach enhances the accuracy of sequential recommendations. Furthermore, a gating mechanism is designed to tailor the global context information to individual user preferences. Extensive experiments on Taobao, Books and Games datasets have shown that HyperCLR consistently surpasses baselines, demonstrating the effectiveness of the method. In particular, in comparison to the best baseline methods, HyperCLR demonstrated a 29.1% improvement in performance on the Taobao dataset.

Graph contrast learning for recommendation based on relational graph convolutional neural network

Current knowledge graph-based recommendation methods heavily rely on high-quality knowledge graphs, often falling short in effectively addressing issues such as the cold start problem and heterogeneous noise in user interactions. This leads to biases in user interest and popularity. To overcome these challenges, this paper introduces a novel recommendation approach termed Knowledge-enhanced Perceptive Graph Attention with Graph Contrastive Learning (KPA-GCL), which leverages relational graph convolutional neural networks. The proposed method optimizes the triplet embedding representation of entity-item interactions based on relationships between adjacent entities in a heterogeneous graph. Subsequently, a graph convolutional neural network is employed for enhanced aggregation. Similarity scores from a contrastive view serve as the selection criterion for high-quality embedded representations, facilitating the extraction of refined knowledge subgraphs. Multiple adaptive contrast-loss optimization functions are introduced by combining Bayesian Personalized Ranking (BPR) and hard negative sampling techniques. Comparative experiments are conducted with ten popular existing methods using real public datasets. Results indicate that the KPA-GCL method outperforms compared methods in all datasets based on Recall, NDCG, Precision, and Hit-ratio measures. Furthermore, in terms of mitigating cold start and noise, the KPA-GCL method surpasses other ten methods. This validates the reasonability and effectiveness of KPA-GCL in real-world datasets.

Fuzzy-Logic-Based Cascaded Decentralized Control and Power Quantification of Residential Buildings for Effective Energy Load Management

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users’ unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables.