User Interest Research Articles

Stakeholder perceptions of the Norwegian salmon farming industry and its future challenges

Aquaculture has emerged as the fastest growing sector in global food production, with salmon farming in the lead. Norway is responsible for over half of the world salmon production, but its industry faces significant challenges which must be addressed to achieve sustainability. One key issue is the lack of a unified understanding among stakeholders of how production affects the environment and intersects with the interests of other users. Given this context, this present study aims to understand the diverse perspectives of different groups of stakeholders regarding the expansion challenges faced by the Norwegian salmon farming industry. We assessed environmental, socio-economic, operational, and regulatory aspects, highlighting areas of agreement and disagreement among stakeholders using Q-methodology. Our results reveal four distinct stakeholder groups, two of which reveal polarized views, while the other two present more balanced opinions. We believe that our findings facilitate enriched dialogues among stakeholders and provide insights into the management of the industry, while addressing the dilemma of ensuring sustainability amid pressures for production growth.

Analysis of Field Work Practice Information System Service Quality Using The Webqual 4.0 Method and Importance Performance Analysis

In the current digital era, the quality of website services is a crucial factor in supporting the effectiveness and efficiency of information systems, including the Information Systems Study Program Field Work Information System (SIPKL) at Universitas Terbuka. However, currently there is no in-depth evaluation of the quality of SIPKL services from a user perspective. This research aims to review the service quality of the SIPKL website as a whole and measure the level of user satisfaction with the services provided. To achieve this goal, the WebQual 4.0 method is used which measures three main dimensions of service quality, namely usability, information quality, and interaction quality. In addition, the Importance Performance Analysis (IPA) method is applied to evaluate the importance and performance of each service attribute being measured, so as to identify areas that require improvement. Data was collected through a survey with 100 respondents from Information Systems study program students who had used the SIPKL website. The research results show a value of 101.6% for the level of conformity, which indicates that the SIPKL website service performance has met or even exceeded user expectations and interests. Meanwhile, the gap value is categorized as "Good" with a positive value of 0.08 or >0. Indicators that require improvement are in quadrants II and III. Overall, this research provides strategic recommendations for SIPKL website managers to improve service quality so that it is more optimal in supporting students' needs in undergoing PKL.

Impact of Digital Payment Factors on Users of the GoPay Digital Payment Platform

This research aims to analyze impact of digital payment factors on users of the GoPay digital payment platform. This research is included in the type of quantitative research. The population in the study is users of the GoPay digital payment platform. Because the exact number of users of the GoPay digital payment platform is unknown, a saturated sample is used, so the sample for this study was 100 respondents. Hypothesis testing in this study uses multiple linear regression analysis. The results of the study show that the GoPay digital payment platform is an electronic wallet developed by PT GoTo Gojek Tokopedia Tbk which is used as a payment service while using the Gojek application. PT GoTo Gojek Tokopedia Tbk started its business from a motorbike transportation service which then expanded its business network by offering various services. The GoPay digital payment platform is one form of financial technology innovation. Security has a significant effect on the interest of users of the GoPay digital payment platform. Convenience has a significant effect on the interest of users of the GoPay digital payment platform.

Let long-term interests talk: An disentangled learning model for recommendation based on short-term interests generation

In e-commerce recommendation systems, users’ long-term and short-term interests jointly influence product selection. However, the behavioral conformity phenomenon tends to be more prominent in short-term sequences, and the entanglement of true preference and popularity conformity data confuses the user’s real interest needs. To address this issue, we propose a sequential recommendation model called DFRec to disentangle short-term interests from popularity bias. By leveraging long-term interest trends, the model promotes the separation of short-term interests from popularity-driven deviations, thereby reducing the impact of popularity interference in short-term sequences. Firstly, we propose a Disentangled Frequency Attention Network(DFAN) to address the entanglement between real sequence features and conformity data in users’ short-term behavioral sequences. The approach clarify the non-entangled representation of the user’s short-term interest and conformity on the basis of long-term interest trends. Secondly, in order to capture the real long-term interest characteristics of users, this paper suggests using a Learnable Filter(LF) to filter the noise frequencies in long-term sequence. The method decouples the horizontal and vertical directions of the sequence and filters out the noise in both directions. Finally, consider the importance of the two interests characteristics is dynamic, we propose a joint learning framework with dual embeddings to balance and fusion these two features of users’ interests. Experimental results on three public datasets demonstrate that our model effectively captures dynamic user interests and outperforms six baseline models.

Means of attracting and holding attention in personalized video blogs of the demonstration type

INTRODUCTION. The active development of the audiovisual sector of new media in a highly competitive environment suggests that content authors are actively working on developing means of attracting and retaining the audience’s attention to a specific work. This process is especially vividly realized in personalized video blogs of the demonstration type, where the author, as a rule, remains one-on-one with the audience. Therefore, it seems appropriate to analyze the means that video bloggers use in their works in order to ensure their popularity.MATERIALS AND METHODS. The empirical basis of the study was formed by publications on the channels “The Kate Clapp” and “Sasha Spilberg” on the YouTube platform from September 1, 2023 to September 1, 2024. The study was carried out based on textual, comparative-typological methods and the method of content analysis.RESULTS AND DISCUSSION. In the course of the study, we came to the conclusion that bloggers work extremely actively with both visual and auditory submodalities. Frequently used visual submodalities include location, size, playing with color, and the use of graphic elements. Auditory submodalities include volume and alternation of speech, music, and noise. We also managed to establish a relationship between the frequency of using visual and auditory submodalities: as a rule, a blogger who actively works with one more often turns to the other. The most popular submodalities are those common to online video content and traditional television. However, in the field of video blogging, their concentration in a work is, on average, higher than on a television screen.CONCLUSION. Submodalities allow the blogger to attract and hold the audience’s attention to the work. The study managed to establish a correlation between the frequency of submodalities and the degree of audience interest in a video fragment: in episodes where the concentration of submodalities is higher, a surge in user interest in media content is noted.

Integrating machine learning and sentiment analysis in movie recommendation systems

The fast growth of the film business, along with an ever-increasing number of movie options, has highlighted the need for better recommendation algorithms. This study investigates the application of sentiment analysis in a movie recommendation system with the goal of improving the user experience. The importance of this sector stems from its ability to bridge the gap between user interests and the vast number of cinematic products, addressing individual emotional states and preferences. Researchers choose to generate movie recommendations based on the sentiments conveyed by viewers’ reviews of the movies. Sentiment-based movie recommendation system research employs techniques such as natural language processing and hybrid models with the goal of increasing user satisfaction. To this purpose, the integration of advanced machine learning algorithms such as cosine similarity, support vector machine, and Naive Bayes improves recommendation systems with sentiment analysis. Cosine similarity improves movie recommendations by recognizing minor user preferences, while support vector machines and Naive Bayes enhance sentiment analysis by offering a nuanced interpretation of textual attitudes. Through trials, the proposed system employs two public datasets for sentiment analysis, namely the TMDB5k dataset and the Reviews dataset, and makes predictions (positive, negative, or neutral) based on the content of the review through conducting sentiment analysis on the text using the Viscous Accretion Disk Evolution Resource (VADER) approach. The findings, based on users’ feedback, are more accurate and informative regarding movie quality, where SVM accuracy is 99.28% and Naïve Bayes accuracy is 96.60% when used with VADER sentiment analysis.

Focus on user micro multi-behavioral states: Time-sensitive User Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach

In recommender systems, user behavior conversion implies user interest drifts and behavior patterns. However, current research has paid little attention to the correlation between target behavior conversion rate and user behavior patterns, and the impact of highly time-sensitive multi-behavior analysis on target behavior conversion rate is neglected. Meanwhile, compared to normal behavior conversions, user deviant behavior conversions are seldom studied. The behavior conversion rate that balances normal behavior patterns and deviant behavior patterns can more accurately reflect user interest drifts and real-time needs, thereby improving recommendation performance. Based on the above motivations, we propose a Time-sensitive Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach (TCMR), aiming to achieve more accurate and adaptive recommendations by analyzing user interest drifts, demand timings and behavior stability. First, we construct a hyper-behavior spatial model of highly collaborative temporal signals, and propose a subnet collaborative method to obtain normal behavior patterns, in which, core subnet, similarity subnet and behavior subnet are extracted from the hyper-behavior spatial model. Subsequently, we design a multi-level user behavior trajectory tree to perceive potential user deviant behaviors by comparing behavior conversions within the single behavior modality and across different behavior modality. By integrating normal behaviors and deviant behaviors, we evaluate user interest drifts, demand timings, and behavior stability, and ultimately obtain a prediction of behavior conversion rate. Finally, a multi-perspective asynchronous reinforcement learning is proposed, enabling TCMR to provide recommendations by considering multiple user perspectives and purposes. Experimental results demonstrate that TCMR exhibits superior recommendation performance and effectiveness.

Intelligence-assisted college English teaching: The application of artificial intelligence technology in personalized learning path design

English learning is an integrated learning process in which listening, speaking, reading, writing and other parts are interrelated. Because of this feature of English learning, higher requirements are put forward for the design of English learning assistance systems. On the other hand, the wide application of advanced science and technology such as automation and cloud computing in all walks of life has promoted the development of society into the era of artificial intelligence. Under such a large development background, how to build a multifunctional integrated English learning system to realize the personalized learning of learners with different levels and different needs has become a focus of current English teaching research. With the increasing abundance of English learning materials, it has become very important to help users find suitable English learning resources from the massive materials, which directly affects the cost of learning time and interest of users. Based on this, this paper introduces a multi-similarity ranking model to achieve personalized design, and then uses artificial intelligence technology to design and implement a personalized intelligent assistant system for college English teaching.

A dynamic preference recommendation model based on spatiotemporal knowledge graphs

Recommender systems are of increasing importance owing to the growth of social networks and the complexity of user behavior, and cater to the personalized needs of users. To improve recommendation performance, several methods have emerged and made a combination of knowledge graphs and recommender systems. However, the majority of approaches faces issues like overlooking spatiotemporal features and lacking dynamic modeling. The former restricts the flexibility of recommendations, while the latter renders recommendations unable to adapt to the changing interests of users. To overcome these limitations, a novel dynamic preference recommendation model based on spatiotemporal knowledge graphs (DRSKG), which captures preferences dynamically, is proposed in this paper. Constructed by knowledge graphs, the model integrates spatiotemporal features and takes into account the dynamic preferences of users across various temporal, spatial, and situational contexts. Therefore, DRSKG not only describes the spatiotemporal characteristics of user behaviors more accurately but also models the evolution of dynamic preferences in spatiotemporal changes. Massive experiments demonstrate that the proposed model exhibits significant recommendation enhancement compared with the traditional one, achieving up to 7% and 5% improvements in terms of Precision and Recall metrics, respectively.

Identifying the gap in assessing activities of daily living in resource-constrained rural settings: An integrative review of existing frameworks and instruments

Introduction: The non-availability of indoor piped water and electricity results in alternate forms of personal care and domestic tasks in resource-constrained rural settings. This article examines the applicability of existing measures for the contextual assessment of basic and instrumental Activities of Daily Living (bADLs and iADLs) in these settings. Method: An integrative review guided by the approach of Lubbe et al (2020) was conducted. Structured database searches of CINAHL, Scopus and Sabinet identified published articles which were subjected to eligibility criteria. Microsoft Excel was used to synthesize data. Results: The search strategy yielded 591 articles that met the inclusion criteria, from which 187 ADL instruments were identified. Three instruments suited to resource-constrained rural settings were identified. Conclusion: Occupational therapists should consider that existing ADL frameworks and instruments appear silent on the impact of limited access to household amenities in resource-constrained settings. This constitutes epistemic injustice as many rural households globally do not have potable water or adequate household energy supply. Global South occupational therapy curricula must include contextually relevant ADL frameworks and development of contextually relevant instruments should be prioritised. Implications for Practice The findings suggest that existing ADL instruments have limitations when utilised in the Global South, particularly in resource-constrained rural settings. As such, occupational therapists practicing in such settings need to interpret instrument scores with caution and apply contextual clinical reasoning in the best interests of service users. Furthermore, training institutions in the Global South must ensure that the limitations of existing ADL frameworks, models and instruments are made overt within curricula and that research efforts are directed towards the development of contextually relevant ADL instruments.

Knowledge-constrained interest-aware multi-behavior recommendation with behavior pattern identification

Recommender systems aim to accurately capture user preferences based on interacted items. Conventional recommender systems mainly rely on the singular-type behavior of users, which may limit their ability to handle practical scenarios (e.g., E-commerce). In contrast, multi-type behavior recommendation (MBR) exploits auxiliary types of behaviors (e.g., view, cart), as well as the target behavior (e.g., buy), and has proven to be an effective way to identify user preferences from various perspectives. Existing MBR methods assume that all auxiliary behaviors of a user have a positive relevance with the target behavior. However, users may not interact with items using all available behaviors, but the degree of relatedness is not explicitly taken into account. To address the issue, we propose a Knowledge-constrained Interest-aware Framework with Behavior Pattern Identification (KIPI). The proposed model identifies user-specific behavior patterns by introducing pair-wise dependency modeling to explicitly reflect the fine-grained relatedness between behavior pairs. Additionally, we enhance item representations by leveraging both instance-view knowledge graph (KG) and ontology-view KG, which provides broader concept information of items. Moreover, we design a concept-constrained Bayesian Personalized Ranking loss to reflect a user's general interest. Extensive studies on the real-world datasets demonstrate that our model outperforms state-of-the-art baselines.

Utilidad de los portales en línea de ciencia ciudadana para estudios florísticos: Orquídeas de Santa María Yucuhiti, Oaxaca, México

Background: The beauty of orchids attracts the interest of users of citizen science portals, turning them into additional sources of information for the study of biodiversity. Currently, iNaturalist records observations of almost 70 % of Mexican orchid species. Here, we evaluate the usefulness of the platform to create a local floristic list of this group of plants. Question: Does the information available on iNaturalist allow for the creation of a local orchid checklist as a conventional floristic study does? Study site and dates: Observations recorded between 2007 and 2024 in Santa María Yucuhiti, Oaxaca, Mexico. Methods: Orchid observations in iNaturalist were reviewed and curated, assigning vegetation type and site elevation for each record. The resulting floristic checklist was commented and compared, using a richness index, with those of other regions of Oaxaca, that were prepared through conventional floristic studies. Results: The study area holds 107 species of orchids, 50 % are endemic to Mexico. The highest richness is found between 1,500-1,999 m elevation and in the oak forest. Acianthera hintonii, Habenaria tetranema and Prosthechea mulasii represent new records for Oaxaca. The study area showed the highest orchid richness index compared to other regions of Oaxaca. Conclusions: Information from iNaturalist allowed the preparation of the orchid checklist for Santa María Yucuhiti, supported by the validated observations on the platform. This, along with the availability of high-resolution photographs, the temporal continuity in documentation, and the exploration of sites with restricted access, enhances the quality of the checklist.

A generative and discriminative model for diversity-promoting recommendation

Diversity-promoting recommender systems with the goal of recommending diverse and relevant results to users, have received significant attention. However, current studies often face a trade-off: they either recommend highly accurate but homogeneous items or boost diversity at the cost of relevance, making it challenging for users to find truly satisfying recommendations that meet both their obvious and potential needs. To overcome this competitive trade-off, we introduce a unified framework that simultaneously leverages a discriminative model and a generative model. This approach allows us to adjust the focus of learning dynamically. Specifically, our framework uses Variational Graph Auto-Encoders to enhance the diversity of recommendations, while Graph Convolution Networks are employed to ensure high accuracy in predicting user preferences. This dual focus enables our system to deliver recommendations that are both diverse and closely aligned with user interests. Inspired by the quality vs. diversity decomposition of Determinantal Point Process (DPP) kernel, we design the DPP likelihood-based loss function as the joint modeling loss. Extensive experiments on three real-world datasets, demonstrating that the unified framework goes beyond quality-diversity trade-off, i.e., instead of sacrificing accuracy for promoting diversity, the joint modeling actually boosts both metrics.

Trending extinctions: online interest in recently extinct animals

AbstractHalf of all species are predicted to face extinction by the end of the century. Despite this, awareness of certain species' extinctions remains surprisingly muted, highlighting the need to improve and quantify public awareness. We explore the connection between biodiversity loss and public awareness by quantifying the changes in online interest on Twitter (now known as X) and Wikipedia both before and after the extinctions of eight species and following their reclassification in the IUCN Red List. Our findings reveal that extinction announcements generally spike online interest for most species, albeit briefly on Twitter (i.e. tweets and retweets about species), while Wikipedia (i.e. article pageviews of species) exhibits a more prolonged interest. IUCN reports were generally not associated with increased interest. Coordinated media coverage, especially when aligned with broader environmental narratives and key events, enhanced the impact of extinction announcements. On Twitter, spatially we observed a shift from local to global interest of users following extinction. We also found a small subset of influential users on Twitter, including content creators and media organisations, who disproportionately shaped conservation discussions. Environmentally oriented individuals and organisations also play a significant role, collectively comprising a third of the top retweeted users. Overall, these results highlight the need for conservation bodies, such as the IUCN, to engage more directly both with media organisations and content creators in order to drive public interest for conservation efforts, especially for less charismatic species. The tragedy of species extinction necessitates such efforts to ensure sustained and meaningful public awareness. Our findings also show that while social media outlets can generate global interest quickly, which may drive public discussions regarding ongoing extinctions and potential future de‐extinctions, this is likely to be short‐lived, underscoring the importance of alternative platforms such as Wikipedia, which can foster longer‐term engagement.

The practice of protecting agricultural land in Jiangsu Province of China

The safeguarding of agricultural land is rooted in national land surveys and remote sensing data, which are enhanced by contemporary information technology. This framework facilitates the monitoring and regulation of unauthorized alterations in cultivated land usage. This paper aims to analyze land policies at the national, provincial, and local levels, investigate the cultivated land protection strategies implemented within the research region, where the policies have gained societal acceptance, and propose recommendations and countermeasures to enhance the development and utilization of land resources. The central issue of this study is to identify the challenges in achieving a balance between human activities and natural ecosystems. To address this issue, the research employs a combination of literature review, semi-structured interviews, text analysis, and content analysis, emphasizing the integration of empirical fieldwork and theoretical frameworks. Key areas of focus include: (a) the current state of the farmland protection system, (b) the legal foundations for local enforcement, (c) the systematic mechanisms for implementing arable land protection, and (d) the coordinated oversight system involving both the Party and government. Notably, the practice of cultivated land protection faces several challenges, primarily stemming from two factors. Firstly, there exists a disconnect between the economic interests of certain illegal land users and the objectives of land management, which hinders effective enforcement. Secondly, environmental repercussions arise from misinterpretations of land policy or non-compliant land development practices aimed at profit, which contradict the goals of ecological sustainability. The study examines two approaches to address the issue: the distribution and effective use of land resources, and the capacity for monitoring and early warning systems. Findings indicate that Dongtai City in Jiangsu Province has rigorously implemented all national land management policies, while also preserving the adaptability of local townships in practical applications, thereby ensuring the consistency of both the quality and quantity of arable land.

Multi-granularity label-aware user interest modeling for news recommendation

Multi-granularity label-aware user interest modeling for news recommendation

Analysis of Google users' interests in information about pacifiers: an ecological digital study.

To determine the interests of Google users from different countries about pacifier-related information over time, through a quantitative and qualitative analysis. This infodemiology longitudinal retrospective ecological study investigated pacifier-related computational metadata from 22 countries using Google Trends platform. Initially, Relative Search Volume (RSV) time series data and their respective main queries were collected employing the topic "pacifier - subject" as the search strategy from January 2004 to June 2024, considering all categories. Data were then analyzed through seasonal decomposition to determine the trend, seasonal, and residual components of the collected time series using Python 3 programming libraries in a Google Collaboratory interface. Additionally, Mann-Kendall tests were performed to access the significance level of trends observed from the seasonal decomposition. Finally, the queries were qualitatively evaluated to identify the main subjects related to users' interests. Statistical significance was determined with p values < 0.05. In general, RSV time series curves exhibited upward trends in all analyzed countries over the years, with significance levels demonstrated by the Mann-Kendall tests. Moreover, queries often centered around types of pacifiers, encompassing searches specifically for certain commercial brands. Some searches concerning newborn and baby care were also observed, particularly highlighting breastfeeding practices. Google users have shown an increasing interest in pacifier-related topics. These data can help understand the needs and concerns of parents and caregivers regarding pacifier use, ultimately supporting the development of effective educational strategies.

The determinants of funding liquidity risk in decentralized lending

Decentralized lending in the DeFi ecosystem mirrors traditional financial intermediation but poses significant risks, particularly funding liquidity risk, due to the volatility and composbility of digital assets, high leverage, and the absence of regulatory protections. This study applies traditional financial intermediation theories to DeFi lending and empirically test which internal factors such as interest rates and user market power, as well as external factors like the USD Index, influence funding liquidity risk in DeFi lending. Analyzing high-frequency blockchain data using the ARDL model and a novel dynamic ARDL simulation from major pools such as Wrapped Bitcoin (WBTC) and Wrapped Ethereum (WETH), the research finds that current algorithmic interest rate models fail to function as effective self-stabilization mechanisms. Additionally, lower deposit concentration in these pools may exacerbate, rather than mitigate, funding liquidity risk.

MixEI: Mixing explicit and implicit commonsense knowledge in open-domain dialogue response generation

The inadequate awareness of real-world knowledge often causes machines to produce generic responses, such as ‘I think so.’, which may bring the degression of user interests. Consequently, enriching knowledge awareness and fabricating informative responses are long-standing challenges in open-domain dialogue systems. Previous studies have shown incorporating everyday commonsense knowledge can significantly enhance the open-domain dialogue response models. Nonetheless, previous works can only use explicit or implicit knowledge. Unlike them, this work presents a novel MixEI to leverage both explicit and implicit commonsense knowledge in dialogue generation. MixEI uses Dual-Way Knowledge Alignment and MixEI-Ranker to retrieve a set of contextually relevant commonsense facts as the explicit background knowledge and identify implicit knowledge labels by selecting clue facts that can tightly connect the dialogue context. MixEI uses BART as the backbone. After jointly encoding the background knowledge and dialogue history, MixEI first tries to externalize the implicit clue knowledge; then, the response decoding can seek information from both explicit and implicit knowledge. Extensive experiments on Chinese Weibo and English Reddit have verified the superior performance of the proposed MixEI-Ranker and MixEI.

Extracting Political Interest Model from Interaction Data Based on Novel Word-level Bias Assignment

In democratic countries, political interest is deeply involved in people's daily lives. Research in political consumerism shows that product purchase decision is also influenced by the political orientation of the consumer. In traditional recommendation system design, user interest in an item is provided by a unified model. Recently, interest disentanglement methods have been introduced. It is shown that by disentangling interest factors such as conformity and private interest, recommendation performance can be significantly improved. However, few studies attempt to disentangle political interest in purchase behavior, which is bipolar. In this paper, we propose a method to extract political interest model from e-commerce interaction data, which is supported by a novel word-level political bias assignment. For the bias assignment part, we improved a political bias distilling method. For the political interest model extraction part, we extend a one-side bias method to make it support bipolar bias. We compare our method with state-of-the-art baseline methods in several evaluation settings, and the experimental results show that our method can achieve superior performance. Further investigation shows that our method is consistent with theories of political consumerism.