User Interest Research Articles

Sentiment Analysis for Movie Database Management System Using Naive Bayes Algorithm

Abstract: According to F. Furtado and A. Singh (2021), movie recommendation systems aim to assist viewers by suggesting movies to watch, thereby avoiding the time-consuming and complex process of selecting from a vast library of movies, which can number in the thousands or even millions. This article seeks to reduce human effort by recommending movies based on user interests. To address these challenges, we propose a technique that combines content-based and collaborative approaches. This method produces more precise results than systems that rely solely on content-based methods. Content-based recommendation systems are limited to individual preferences and do not recommend items outside the user’s immediate interests, restricting exploration. To overcome these limitations, we developed a system that addresses these concerns. The proposed movie recommendation system uses Cosine Similarity to suggest movies like the one chosen by the user. While existing algorithms generate recommendations, they often fail to answer the critical question, “Is this a film worth watching?” To enhance the overall experience, our system incorporates sentiment analysis of selected movie reviews using machine learning techniques. This study employs two supervised learning approaches, Naïve Bayes (NB) and Support Vector Machine (SVM), to improve accuracy and efficacy. A comparison of the two methods reveals that SVM achieved an accuracy score of 98.63%, while NB scored 97.33%, as reported by N. Pavitha et al. (2022). The project integrates a sentiment analysis module into the movie management system to enhance decision-making and user experience. The Naïve Bayes approach is used to classify and evaluate user reviews and feedback into positive, negative, and neutral categories. This analysis provides insights into audience preferences, overall satisfaction, and movie reception. The system utilizes this information to refine movie recommendations, analyze trends, and enhance content selection, all of which contribute to a more personalized user experience. The system is evaluated using the ISO 25010 framework, focusing on functionality, reliability, usability, effectiveness, and robustness. Findings show that respondents actively participated in the review process across multiple domains. Key results indicate high satisfaction levels in terms of ease of use, interface design, and system performance. Feedback also highlighted areas for improvement, such as optimizing loading times and enhancing specific features for a smoother user experience. Overall, the findings reflect positive engagement with the system, demonstrating its effectiveness in meeting user needs while identifying opportunities for future enhancements.

Enhancing intelligent marketing systems: a multi-layer hypernetwork approach integrating evidence theory for influential node identification

PurposeThis study proposes an intelligent marketing system model based on a combination of multi-layer hypernetworks and evidence theory, aiming to address the shortcomings of traditional marketing models in accurately identifying key nodes. We propose a new method to improve the accuracy and response speed of intelligent marketing systems by combining evidence theory with multi-layer hypernetworks. We conducted an experiment using a certain car brand (SUV) as an example, which has a wide customer base in both domestic and international markets and has branches in multiple countries. By analyzing its sales data and user behavior, we evaluated the potential reduction in advertising costs and improvement in user satisfaction that may result from adopting this model.Design/methodology/approachThe proposed model begins with the development of a user interest model, which is subsequently converted into a user label model based on user behavior and a rating matrix. A multi-layer aggregation hypernetwork is then constructed to define the network’s topology. An identification framework is established using evidence theory, and the Dempster–Shafer (D-S) evidence combination method is applied to integrate local, positional and global network indicators. Simulation experiments are conducted to evaluate the model’s performance.FindingsThis study proposes an intelligent marketing system model that integrates multi-layer hypernetworks with Dempster–Shafer evidence theory to address the limitations of traditional marketing models in identifying influential nodes. The proposed model is tested in the automotive industry, specifically using sales and user behavior data from a well-known SUV brand operating globally. This industry provides a complex and competitive environment ideal for validating the model’s ability to improve marketing precision. The results demonstrate that the model significantly enhances the accuracy of key node identification, reduces advertising costs by 10–15% and improves customer satisfaction scores to over 90%. Furthermore, preliminary experiments in the retail and e-commerce sectors highlight the model’s adaptability and potential for broader application. By combining local, positional and global indicators, the model effectively optimizes marketing strategies, providing a novel framework for intelligent decision-making in diverse industries. This study selected a well-known SUV car brand as the experimental subject. This brand mainly sells SUV models and has a wide customer base worldwide. Its products are known for their high performance and reliability. The brand has millions of customers, and its main markets include North America, Europe and Asia. It has branches in multiple countries and has significant international influence. According to publicly available data, the brand’s annual revenue reaches billions of dollars.Originality/valueThe main contribution of the research is the proposal of a novel intelligent marketing optimization framework based on multi-layer hypernetworks and evidence theory, which can effectively solve the problems of data silos and information asymmetry faced in traditional marketing systems.

Research on Personalized Design and Recommendation Systems for Cultural and Creative Products Based on User Behavior Data

Personalized recommendation technology involves the process of soliciting user data and prescribing a user interest model and active recommendations for some users. Such kinds of creative products involve subjective judgments and intricate patterns in the aesthetics, and it is always difficult to encode algorithms that are capable of understanding and recommending these subjective aspects. In this paper, the beetle swarm optimization algorithm is incorporated into a refined deep neural network model called the beetle swarm-drive refined deep neural network (BS-RDNN) for the analysis of personalized design and recommendation systems for user behaviors. Information about user behavior and feedback was collected as part of this study. The data were preprocessed using Min-Max normalization. t-distributed stochastic neighbor embedding (t-SNE) is employed to reduce the dataset dimensions. The proposed method is discussed with other types of recommendation algorithms. The proposed method is implemented with the aid of Python software. This result proves that the BS-RDNN method has better performance in terms of precision (91.34%), accuracy (93.24%), F1-score (92.23%), recall (92.44%), AUC (91.42%), and overall satisfaction of the users. Therefore, the use of the suggested system to coordinate with the design ideas of different individuals can benefit the field of cultural and creative products.

Efficiently Exploiting Muti-Level User Initial Intent for Session-Based Recommendation

Session-based recommendation (SBR) aims to predict potential user interactions within an anonymous session. It utilizes learned user interests to recommend items. As research has progressed, researchers have shifted towards exploring user initial intent, which can provide practical guidance for item selection. However, a significant limitation of the current methodologies is that they often assume the first item in a session as the initial intent, neglecting the possibility of a random initial click. Additionally, these methods typically merge the initial intent with the session representation without considering dynamic changes in user interests. To address these challenges, we propose an innovative approach named Efficiently Exploiting Muti-level User Initial Intent for (EMUI) for session-based recommendation. This approach includes a multi-level initial-intent generation module (MIGM) and an interest matching module (IMM). Specifically, the MIGM is designed to extract a more comprehensive representation of user initial intent from various levels, effectively mitigating the issue of random initial clicks. Furthermore, we propose the IMM to ensure alignment between dynamic interests and user initial intent. The IMM identifies components within multi-level user initial intent that correlate with dynamic interests, thereby enhancing session representation and, ultimately, improving recommendation performance. In addition, in order to optimize the initial user intent at each level, we introduce a contrastive learning task to maximize the use of user initial intent at each level. A considerable number of experiments on three real-world datasets have shown that our EMUI has significantly enhanced the recommendation accuracy over state-of-the-art methods.

A refined pushing method for financial product marketing data based on user interest mining

A refined pushing method for financial product marketing data based on user interest mining

Mobile-Based Event Decoration Ordering System Using UAT Method with PIECES Framework

The Mobile Event Decoration Booking System is an innovative solution designed to facilitate users in ordering event decorations. By implementing the User Acceptance Testing (UAT) method and the PIECES framework, this system ensures that the developed application meets the needs and expectations of users. This research aims to identify and analyze key features in the ordering process and evaluate user satisfaction with the application. Respondents provide valuable feedback regarding the interface, functionality, and overall user experience through UAT. The research results indicate that this application can enhance the efficiency of bookings, reduce communication errors between service providers and customers, and offer a better experience. With the application of the UAT method, users feel that this system effectively meets their needs, resulting in an improved experience in event planning. These findings suggest that the factors influencing user satisfaction and interest are adequate and should be maintained. The Mobile Event Decoration Booking System has successfully improved the efficiency and effectiveness of the booking service, with an average user satisfaction rate of 95%.

Web Design and Consumer Repurchase Intention: The Roles of E-Satisfaction

Objective: Repurchase Intention (RI) becomes one of the frame aspects of customer retention. This study aims to investigate the impact of Web Design (WD) on customers Repurchase Intention (RI) and to evaluate whether E-Satisfaction (ES) serves as a mediator in this relationship. Methods: The research employed a quantitative approach, utilizing purposive sampling to gather data sourced from 124 students from several colleges in Semarang. It conducted a Structural Equation Modeling (SEM) analysis using SmartPLS. Result: The findings of this research show that Web Design (WD) has a significant and positive effect on Repurchase Intention (RI). Additionally, E-Satisfaction (ES) positively influences Repurchase Intention (RI) and serves as a mediator for the beneficial impact of Web Design (WD) on Repurchase Intention (RI). Novelty: The findings of this study provide recommendations for Bukalapak to enhance its Web Design (WD) and ensure high levels of user E-Satisfaction (ES). These strategies can help boost user interest in making repeat purchases on the Bukalapak platform. The novelty in this study lies in the use of diverse range of objects and research methods that are different from previous studies.

DualCFGL: dual-channel fusion global and local features for sequential recommendation

Sequential recommendation systems capture the dynamic interests of users and predict their future preferences. A noteworthy problem in sequential recommendation is coping with the intrinsic changes of user interests. The sequence of user interactions is generated by more than a single and stable global preference, users may have interest drift that occur in a short period of time. We call this short-term interest drift as the local preference of users, which is often a key factor affecting the final choice of users. However, existing methods have limitations in observing local preferences, which leads to an incomplete consideration of the local preferences. Moreover, using a single model to represent global–local preferences obscure the distinct features of each, limiting the potential synergistic benefits. To alleviate the above limitations, we propose a novel model with a dual-channel structure to monitor both global and local preferences and ensure they complement each other. The model extracts the global preferences of users with a bidirectional Transformer using random masking and a sliding window, and extracts the local preferences with a patch-based stacked bottleneck residual convolution. To enable the model to consider both the global and local preferences of users, we design an adaptive orthogonal fusion module, which effectively fuses the two preferences and enables the two feature types to complement and enhance each other. We integrate the fused user preferences with a knowledge distillation method that further improves the model’s expressive ability. We conduct extensive experiments on three widely used datasets, and the results show that our model outperforms current state-of-the-art models.

Exploring the challenges of e-resources acquisition to enhance access and the enabling factors to embrace patron-driven e-resources acquisition in Ethiopian university libraries

University libraries curate their collections striving to align with the academic disciplines of their respective institutions and to fulfill the diverse information needs of their users. The integration of digital technologies into library operations has significantly impacted their services and activities. Despite the numerous challenges, Ethiopian university libraries attempt to provide access to electronic resources. This paper aims to investigate the obstacles surrounding e-resource acquisition to enhance access and to identify the existing facilitating factors to the embrace patron-driven acquisition within Ethiopian university libraries. This study adopted an explanatory research method with a qualitative approach. The research focused on librarians, library directors, and postgraduate school directors from two public universities in Ethiopia: Jimma and Ambo Universities. A total of 22 participants were selected purposefully, and 19 of them responded accordingly. Data was collected using semi-structured Delphi techniques and interviews and analyzed using QDA Miner Lite software. The study uncovered several challenges hindering e-resource acquisition, including limited user involvement in the acquisition, the absence of an online acquisition system, inadequate acquisition budget, scarcity of relevant content, exorbitant journal costs, foreign currency constraints, and protracted licensing procedures. Conversely, the research identified enabling factors to patron-driven e-resource acquisition adoption, including the presence of ICT infrastructure, the proliferation of postgraduate programs, librarians’ strong willingness and professional readiness to integrate e-resources in the library collection, heightened user interest in e-resources, the prevalence of e-learning practices, initiatives to enhance digital services and supportive management. Therefore, addressing the challenges and leveraging enabling factors is imperative to formulate a strategic plan for effective patron-driven e-resource subscription in Ethiopian university libraries.

Adaptive Intention Learning for Session-based Recommendation

In recent years, session-based recommender systems (SRSs) have emerged as a significant research focus within the recommendation field. Capturing user intentions to infer user interest accordingly has proven to be effective in enhancing the accuracy of SRSs. However, existing techniques assume that all sessions have the same number of intentions or that the items in one category belonging to the same session reflect the same intention. In real applications, such as e-commerce, sessions may have different numbers of intentions, and the same type of items in a session may correspond to different intentions. As a result, existing techniques cannot guarantee high-quality user interest prediction. In this paper, we propose a novel A daptive I ntention L earning N etwork (AILN) to capture an adaptive number of intentions for each session, thereby enhancing the accuracy of user interest inference. Specifically, we design an intention evaluation network (IEN) to evaluate whether a subsequence of a session corresponds to a valid intention, and an intention generation network (IGN) to learn the representation of a valid intention. By checking each subsequence of a session, IEN and IGN enable the incremental learning of a session-specific intention hierarchy (IH) to store valid intentions of the session. To reduce the cost of building the IH, we propose a pruning strategy that exploits the intention validity to avoid unnecessary evaluation. The representative intentions are selected from IH and input into a designed interest predictor to infer the user interest. Experimental results on two real-world datasets demonstrate the superiority of our proposed AILN.

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

Antecedentes: La belleza de las orquídeas atrae el interés de usuarios de portales de ciencia ciudadana, convirtiéndolos en fuentes adicionales de información para estudiar la biodiversidad. Actualmente iNaturalist registra observaciones para casi 70 % de las especies de orquídeas mexicanas, aquí evaluamos su utilidad para elaborar un listado florístico local con ese grupo de plantas. Preguntas: ¿la información disponible en iNaturalist permite elaborar un listado florístico local de orquídeas como lo hace un estudio florístico convencional? Sitio y años de estudio: Observaciones registradas entre 2007 y 2024 en Santa María Yucuhiti, Oaxaca, México. Métodos: Las observaciones de orquídeas en iNaturalist fueron revisadas y curadas, asignando tipo de vegetación y elevación del sitio de cada registro. El listado resultante se comparó, mediante un índice de riqueza con los de otras regiones de Oaxaca obtenidos mediante estudios florísticos convencionales. Resultados: La zona estudiada alberga 107 especies de orquídeas, 50 % de ellas endémicas de México. La mayor riqueza se presenta entre 1,500-1,999 m de elevación y el bosque de encino. Acianthera hintonii, Habenaria tetranema y Prosthechea mulasii constituyen nuevos registros para Oaxaca. La zona de estudio presentó el mayor índice de riqueza de orquídeas en comparación con otras regiones del estado. Conclusiones: La información de iNaturalist permitió elaborar el listado de orquídeas de Santa María Yucuhiti, respaldado por observaciones validadas en la plataforma. Esto y la disponibilidad de fotografías con buena resolución, continuidad temporal en la documentación y exploración de sitios con acceso restringido favorecen la calidad de un listado.

E-learning Recommender System based on k-Nearest Neighbor (KNN), Singular Value Decomposition (SVD), and CoClustering Approaches

The advent of the digital age resulted in the development of the e-learning platform as a helpful resource for top-notch e-learning materials. Despite its promise, the entire scope of its potential hasn't been ultimately discovered. E-learning platforms are expected to offer information that meets customers' requirements and interests to draw users and boost profits. These suggestions are created by considering various aspects, including previous browsing habits, purchase history, demographic data, and others. E-learning platforms may improve the quality of the learning process by offering users interesting information tailored to their specific requirements and preferences by utilizing cutting-edge technology. This paper explores Machine Learning (ML) approaches used in e-learning content recommender platforms. Three ML approaches are chosen and applied to predict user interest and need Singular Value Decomposition (SVD), k-nearest Neighbor Baseline (KNNBaseline), and CoClustering. These ML approaches boost user experience by analyzing data trends and patterns of usage to deliver insights into the best customized and appropriate educational materials for every user. The previous user ratings trend is employed to derive the item ratings. Mean absolute error (MAE) and root mean square error (RMSE) are assessed to evaluate the implemented techniques' effectiveness.

Teknik copywriting pada media sosial perpustakaan di Pontianak sebagai strategi pemasaran digital

Introduction. This paper aims to identify copywriting techniques used in managing Instagram media of various libraries in Pontianak and the extent of their impact on the target users’ decision to use the library as a learning resource. Data Collection Methods. We used a qualitative approach with virtual observation, literature studies, semi-structured interviews with eight users as participants. Data Analysis. We used data reduction, data presentation to draw conclusion. We used triangulation, cross-checking on key informants and specialists to validate the results. Results and Discussion. Copywriting techniques have not been carried out by all library managers so that the AIDA formula is not visible in every instagram content. Inconsistency in the application of copywriting formula affects the interest of library users in seeing to the information delivered through instagram content. The inconsistency was influenced by librarian’s knowledge and copywriting skills. The library received a positive response marked by library visits, collection utilization, and user participation which increased (although not significantly) because marketing strategies need to be improved. Conclusion. Library managers still focus on quantity and pay less attention to the quality of instagram content so that improving copywriting skills is less of a concern.

FAKTOR-FAKTOR YANG MEMPENGARUHI KUALITAS PELAYANAN ADMINISTRASI PADA KANTOR KELURAHAN PULANG PISAU

Public service is an activity carried out in order to fulfill basic needs in accordance with the basic rights of every citizen for an administrative service provided by a service provider related to the public interest. The purpose of this article is to determine the factors that influence the quality of administrative services and the role of the village government in improving the quality of services to the community at the Pulang Pisau Village Office as one of the government agencies that has the authority to provide services. Research using qualitative methods that emphasize the analysis of inductive thinking processes with the dynamics of the relationship between observed phenomena and always using scientific logic. The results of the study show the quality of administrative services at the Pulang Pisau Village Office using the five indicators of the SERVQYAL (Service Quality) model, only one indicator that works well is reliability. This is due to the accuracy of officers, clear service standards and the ability of officers to use aids and officer expertise. However, four indicators that are still not running well are Physical Evidence (Tangible) the quality of service is still low due to the discomfort of the service location and the discipline of service officers, Response (Responsiveness) that officers in the service process are not smooth and timely, Guarantee (Assurance) there are still costs for bureaucracy in service. And empathy (Emparthy) prioritizes personal interests over the interests of service users.

Digital Abuse: How Dark Patterns Manipulate Our Lives

The digital economy in Russia and abroad is of growing interest to lawmakers, especially in the context of the use of so-called ‘dark patterns' — manipulative interface solutions that influence user behavior. BigTech companies consolidate their dominant position in the market by implementing innovative practices, many of which cannot be recognized as bona fide. The most prominent example of the implementation of user retention mechanisms through embedded features is the Tik Tok platform (Dou Yin). The Tik Tok phenomenon is still being studied by experts, but one of the clues is the unique recommendation feed that dynamically adjusts to the user's interests and is endless, creating the so-called “immersion effect”. The article examines Russian and international approaches to regulating these practices. Particular attention is paid to legislative initiatives and enforcement practices aimed at protecting consumer rights and limiting the use of manipulative practices on digital platforms. The Russian legislation is still focused on certain aspects of consumer protection and countering unfair competition, while Western countries introduce specialized norms to combat “dark patterns”. The aim of the article is to examine the existing norms and suggest ways to adapt successful foreign practices to the Russian legal context.

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.

Certified Unlearning for Federated Recommendation

Recommendation systems play a crucial role in providing web-based suggestion utilities by leveraging user behavior, preferences, and interests. In the context of privacy concerns and the proliferation of handheld devices, federated recommender systems have emerged as a promising solution. These systems allow each client to train a local model and exchange only the model updates with a central server, thus preserving data privacy. However, certain use cases necessitate the deduction of contributions from specific clients, a process known as “unlearning”. Existing machine unlearning methods are designed for centralized settings and do not cater to the collaborative nature of recommendation systems, thereby overlooking their unique characteristics. This paper proposes CFRU, a novel federated recommendation unlearning model that enables efficient and certified removal of target clients from the global model. Instead of retraining the model, our approach rolls back and eliminates the historical updates associated with the target client. To efficiently store the learning process's historical updates, we propose sampling strategies that reduce the number of historical updates, retaining only the most significant ones. Furthermore, we analyze the potential bias introduced by the removal of target clients’ updates at each training round and establish an estimation using the Lipschitz condition. Leveraging this estimation, we propose an efficient iterative scheme to accumulate the bias across all rounds, compensating for the removed updates from the global model and recovering its utility without requiring post-training steps. Extensive experiments conducted on two real-world datasets, incorporating two poison attack scenarios, have shown that our unlearning technique can achieve a model quality that is 99.3% equivalent to retraining the model from scratch while performing up to 1000 times faster.

SIIR: Symmetrical Information Interaction Modeling for News Recommendation.

Accurate matching between user and candidate news plays a fundamental role in news recommendation. Most existing studies capture fine-grained user interests through effective user modeling. Nevertheless, user interest representations are often extracted from multiple history news items, while candidate news representations are learned from specific news items. The asymmetry of information density causes invalid matching of user interests and candidate news, which severely affects the click-through rate prediction for specific candidate news. To resolve the problems mentioned above, we propose a symmetrical information interaction modeling for news recommendation (SIIR) in this article. We first design a light interactive attention network for user (LIAU) modeling to extract user interests related to the candidate news and reduce interference of noise effectively. LIAU overcomes the shortcomings of complex structure and high training costs of conventional interaction-based models and makes full use of domain-specific interest tendencies of users. We then propose a novel heterogeneous graph neural network (HGNN) to enhance candidate news representation through the potential relations among news. HGNN builds a candidate news enhancement scheme without user interaction to further facilitate accurate matching with user interests, which mitigates the cold-start problem effectively. Experiments on two realistic news datasets, i.e., MIND and Adressa, demonstrate that SIIR outperforms the state-of-the-art (SOTA) single-model methods by a large margin.

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.