User Interest Research Articles

MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI Recommendation

Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia, and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) Ignoring personalized spatial- and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users; (2) Insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

Harnessing Text and Web Analytics to Enhance Decision-Making in Job Opportunity Categorization

Text analytics is defined as a method of analyzing compilations of structured text such as dates, times, locations, semi structured text, such as HTML and JSON as well as unstructured text, such as word documents, videos, and images, to extract and discover trends and relationships without requiring the exact words or terms to convey those concepts. Web analytics on the other hand is the technology that collects, measures, analyses, and provides reports of data on how users use websites and web applications. It is used to track a number of aspects of direct user-website interactions, such as the number of visits, time spent on the site, and click pathway. It also aids in the identification of user interest areas and the enhancement of web application features. We used clustering techniques to categorize the job opportunities that are available for the job seekers. By implementing text analytics, text data may be grouped with the goal of providing outcomes in the form of word frequency distribution, pattern identification, and predictive analytics. Text analytics may create one-of-a-kind values to use in the improvement of decision-making and business processes, as well as the development of new business models.

Analisis Kepuasan Pengguna Terhadap Layanan Arsip di Sekolah Vokasi Institut Pertanian Bogor

Service quality is very important, because its role is related to the interests of users, both the interests of internal and external users as a whole. This research aims to determine the level of satisfaction and performance assessment of archive services. The data collection method uses a literature study model and questionnaires. Data analysis uses primary data obtained from the results of questionnaire analysis, and secondary data obtained from a literature review. Respondents in this research were education staff, lecturers and students. The level of satisfaction with the quality of archival services at the IPB College of Vocational Studies based on CSI analysis was 81.76%, indicating that in general respondents were very satisfied with the quality of archival services. The results of the suitability analysis show that the relationship between expectations and the quality of archival services shows the highest gap in indicator A3(-0.502), where respondents expect the complete availability of required records, and the lowest gap in indicator Y3 (-0.364), namely staff politeness and the information provided is clearly understood. The conformity level of 90.24% indicates that the quality of archival services is considered good and service performance is in line with expectations. IPB College of Vocational Studies must consider human resource competency and ease of access. Human resourcecompetency, ease of access, is one of the issues that needs to be paid attention to by the IPB College of Vocational Studies. One of the managerial implications is increasing capabilities in the field of archives and recommendations for a comprehensively integrated system for IPB. It is hoped that researchers and stakeholders will accept development ideas and practical contributions in the form of strategic understanding and references.

Optimizing an English Text Reading Recommendation Model by Integrating Collaborative Filtering Algorithm and FastText Classification Method

To comprehend the genuine reading habits and preferences of diverse user cohorts and furnish tailored reading recommendations, this study introduces an English text reading recommendation model designed specifically for long-tail users. This model integrates collaborative filtering algorithms with the FastText classification method. Initially, the integrated collaborative filtering algorithm is explicated, followed by the calculation of the user's interest distribution across various types of English texts, achieved through an enhanced Ebbinghaus forgetting curve and analysis of user reading behaviors. Subsequently, an intelligent English text reading recommendation is generated by amalgamating collaborative filtering algorithms with association rule-based recommendation algorithms. Through optimization of the recommendation generation process, the model's recommendation accuracy is enhanced, thereby augmenting the performance and user satisfaction of the recommendation system. Finally, a comparative analysis is conducted with respect to the Top-N algorithm model, matrix factorization-based algorithm model, and FastText classification model, illustrating the superior recommendation accuracy and F-Measure value of the proposed model. The study findings indicate that when the recommendation list contains 10, 30, 50, and 70 texts, the recommendation accuracy of the proposed algorithm model is 0.75, 0.79, 0.8, and 0.74, respectively, outperforming other algorithms. Furthermore, as the number of texts increases, the F-Measure of all four models gradually improves, with the final F-Measure of the proposed model reaching 0.81. Notably, the F-Measure of the English text reading recommendation model proposed in this study significantly surpasses that of the other three recommendation methods. Demonstrating commendable performance in recall rate, root mean square error, normalized cumulative gain, precision, and accuracy, the model adeptly reflects user reading interests, thereby enhancing the accuracy of text recommendations and the overall system performance. The study findings offer crucial insights and guidance for enhancing the accuracy and overall efficacy of English text recommendation systems.

Towards more sustainable and trustworthy reporting in machine learning

AbstractWith machine learning (ML) becoming a popular tool across all domains, practitioners are in dire need of comprehensive reporting on the state-of-the-art. Benchmarks and open databases provide helpful insights for many tasks, however suffer from several phenomena: Firstly, they overly focus on prediction quality, which is problematic considering the demand for more sustainability in ML. Depending on the use case at hand, interested users might also face tight resource constraints and thus should be allowed to interact with reporting frameworks, in order to prioritize certain reported characteristics. Furthermore, as some practitioners might not yet be well-skilled in ML, it is important to convey information on a more abstract, comprehensible level. Usability and extendability are key for moving with the state-of-the-art and in order to be trustworthy, frameworks should explicitly address reproducibility. In this work, we analyze established reporting systems under consideration of the aforementioned issues. Afterwards, we propose STREP, our novel framework that aims at overcoming these shortcomings and paves the way towards more sustainable and trustworthy reporting. We use STREP’s (publicly available) implementation to investigate various existing report databases. Our experimental results unveil the need for making reporting more resource-aware and demonstrate our framework’s capabilities of overcoming current reporting limitations. With our work, we want to initiate a paradigm shift in reporting and help with making ML advances more considerate of sustainability and trustworthiness.

Copyright for Computer Programs and Databases

In the digital age, computer programs and databases have emerged as invaluable intellectual properties, driving technological advancements and innovation across industries. As these works become increasingly critical to modern society, ensuring adequate legal protection through copyright law is paramount. This article explores the intricate legal landscape surrounding copyright protection for computer programs and databases. Focusing on the international and national legal frameworks, the article examines the requirements for copyright eligibility, such as originality, fixation, and authorship. It delves into the scope of protection, encompassing source code, object code, user interfaces for computer programs, and the structure, arrangement, and data content of databases. The article also addresses the exceptions and limitations to copyright protection, balancing the rights of creators with the legitimate interests of users and the public. Fair use, reverse engineering, and the creation of backup copies are among the key exceptions discussed. Additionally, the article highlights the challenges of enforcing copyright protection in the digital realm, including digital piracy, transnational infringement, and the complexities surrounding technological protection measures. By providing a comprehensive overview of this intricate legal domain, the article aims to foster a deeper understanding of the critical role copyright plays in incentivizing innovation and creativity in the digital age, while also acknowledging the need for a balanced approach that promotes access and legitimate use of these invaluable works

Borrow Barter Bridge - Hostel Necessities Web Platform

Abstract: We propose the creation of a hostel sharing platform for such necessities in response to the previously described issue of frequent access to necessities in the two aforementioned hostel environments. This platform would fulfill demands ranging from laptops and hair straighteners to belts and kettles, allowing for borrowing and lending of things with a maximum 48-hour return time. In order to begin, each user must first create an account with their personal information, agree to a user policy stating that they are liable for any borrowed stuff, and take pictures of any lent or borrowed objects during the meeting. To penalize offenders, trust points will be awarded for adhering to the policy and banned for breaking it. The number of points taken will increase with each infraction, and offenders will be subject to a justification and fine period after being reinstated. By maximizing resource use and guaranteeing timely meeting of urgent requirements, this platform seeks to promote a culture of accountability and mutual aid while protecting user interests.

Personalized Learning Path Recommendation Algorithm for English Listening Learning

A personalized learning path recommendation algorithm for English listening learning leverages data on users' proficiency levels, learning preferences, and past performance to suggest tailored learning paths. By incorporating natural language processing (NLP) techniques, the algorithm can analyze audio content, transcripts, and user interactions to assess comprehension and identify areas for improvement. It then recommends a sequence of listening exercises, podcasts, audiobooks, or other resources matched to the user's skill level and interests. This paper introduces the Ranked Path Recommendation (RPR) algorithm, designed to facilitate personalized English listening learning. Leveraging data analytics and machine learning techniques, the RPR algorithm aims to provide tailored recommendations of listening materials based on individual learners' preferences, proficiency levels, and learning objectives. Through a series of experiments and analyses, the effectiveness of the algorithm is evaluated, considering factors such as recommendation accuracy, learner satisfaction, and adaptability. Results demonstrate the algorithm's ability to curate diverse and relevant listening materials, enhancing learner engagement and comprehension. However, challenges such as algorithmic biases and the need for ongoing refinement are acknowledged. Ultimately, the RPR algorithm represents a promising approach to adaptive learning in language education, contributing to the advancement of personalized and effective language learning experiences. Results demonstrate that the RPR algorithm achieved recommendation accuracy ranging from 85% to 92% across ten different scenarios, with corresponding learner satisfaction ratings ranging from 6.9 to 8.8 on a scale of 1 to 10. Learner feedback indicates that recommended materials were perceived as relevant, engaging, and diverse, contributing to enhanced comprehension and motivation.

Disentangled Variational Autoencoder for Social Recommendation

AbstractSocial recommendation aims to improve the recommendation performance by learning user interest and social representations from users’ interaction records and social relations. Intuitively, these learned representations entangle user interest factors with social factors because users’ interaction behaviors and social relations affect each other. A high-quality recommender system should provide items to a user according to his/her interest factors. However, most existing social recommendation models aggregate the two kinds of representations indiscriminately, and this kind of aggregation limits their recommendation performance. In this paper, we develop a model called Disentangled Variational autoencoder for Social Recommendation (DVSR) to disentangle interest and social factors from the two kinds of user representations. Firstly, we perform a preliminary analysis of the entangled information on three popular social recommendation datasets. Then, we present the model architecture of DVSR, which is based on the Variational AutoEncoder (VAE) framework. Besides the traditional method of training VAE, we also use contrastive estimation to penalize the mutual information between interest and social factors. Extensive experiments are conducted on three benchmark datasets to evaluate the effectiveness of our model.

On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems

Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significant drawback persists: its poor data efficiency, stemming from its interactive nature. The training of reinforcement learning-based recommender systems demands expensive online interactions to amass adequate trajectories, essential for agents to learn user preferences. This inefficiency renders reinforcement learning-based recommender systems a formidable undertaking, necessitating the exploration of potential solutions. Recent strides in offline reinforcement learning present a new perspective. Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings. Given that recommender systems possess extensive offline datasets, the framework of offline reinforcement learning aligns seamlessly. Despite being a burgeoning field, works centered on recommender systems utilizing offline reinforcement learning remain limited. This survey aims to introduce and delve into offline reinforcement learning within recommender systems, offering an inclusive review of existing literature in this domain. Furthermore, we strive to underscore prevalent challenges, opportunities, and future pathways, poised to propel research in this evolving field.

A Personalized Collaborative Filtering Recommendation System Based on Bi-Graph Embedding and Causal Reasoning

The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users’ historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including “MovieLens-1M” and “Douban dataset” from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems.

Career Guidance using Machine Learning

This study introduces a novel method for career counselling: an application that uses cutting-edge technology to provide individualised suggestions for jobs and skills. People frequently struggle to identify appropriate career options that match their objectives and abilities in today's fast-paced labour market. These difficulties might not be sufficiently addressed by traditional counselling techniques. Our novel approach analyses large-scale job and skill databases using machine learning, natural language processing (NLP), and data analytics to deliver personalised advice based on each user's interests and career aspirations. Our programme makes advantage of these technologies to help users stay ahead in their professional endeavours by providing insights into developing job trends and facilitating informed decision-making.

Redesign User Interface (UI) and User Experience (UX) of Ikan Giling Segar Website with Design Thinking Method to Increase User Interest

In this era, digital transformation in business is increasing. One of the businesses that participated in the transformation and was studied in this research is Sabrina Store, which sells groundfish through its website, Ikan Giling Segar. Even so, challenges arise related to website design that is not optimal. This research is included in the Research and Development (R&D) research type. It uses design thinking methods to improve the appearance of the website design so that user interest also increases. Data were collected through questionnaires and related literature. Pre-questionnaires and questionnaires were conducted after the redesign using the User Experience Questionnaire (UEQ) method with respondents from potential users in East Java, as well as usability testing of the redesigned prototype. Results showed that five aspects of UEQ were below average before the redesign. However, after the redesign, all aspects improved. Usability and UEQ testing also showed that the redesign improved user interest and experience. Thus, using design thinking and R&D methods can provide holistic solutions to improve the UI / UX of online business websites with positive results.

Internal and external subjects of financial control over the reliability of financial statements

The article substantiates the relevance of research into issues of increasing the reliability of financial reporting. In this regard, the formation of an effective system of financial control over accounting reporting is identified as a primary task. To solve this problem, it is necessary to increase the efficiency of control both in the process of generating reports and subsequent control over already compiled reports. Internal control is provided directly by the accounting service and independent internal controllers, including an internal controller, auditor, and auditor. External control is provided by tax services in the process of forming the state information resource of accounting financial statements and audit companies. The article examines the functions of the main elements of the financial control system for accounting reports and suggests directions for improving the efficiency of their activities. Particular attention is paid to internal control and external audit systems, the functioning of which should be aimed at increasing the reliability of financial reporting in the interests of external users.

M 3 Rec: A Context-aware Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn the recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this paper, we address the cold-start challenge in the RL-based recommender systems by proposing a novel context-aware offline meta-level model-based reinforcement learning approach for user adaptation. Our proposed approach learns to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with limited contextual information. To improve adaptation efficiency, our approach learns to recover the user choice function and reward from limited contextual information through an inverse reinforcement learning method, which is used to assist the training of a meta-level recommendation agent. To avoid the need for online interaction, the proposed method is trained using historically collected offline data. Moreover, to tackle the challenge of offline policy training, we introduce a mutual information constraint between the user model and recommendation agent. Evaluation results show the superiority of our developed offline policy learning method when adapting to new users with limited contextual information. In addition, we provide a theoretical analysis of the recommendation performance bound.

Understanding Chinese Internet Users' Perceptions of, and Online Platforms' Compliance with, the Personal Information Protection Law (PIPL)

The Personal Information Protection Law (PIPL) was implemented in November 2021 to safeguard the personal information rights and interests of Internet users in China. However, the impact and existing shortcomings of the PIPL remain unclear, carrying significant implications for policymakers. This study examined privacy policies on 13 online platforms before and after the PIPL. Concurrently, it conducted semi-structured interviews with 30 Chinese Internet users to assess their perceptions of the PIPL. Users were also given tasks to identify non-compliance within the platforms, assessing their ability to address related privacy concerns effectively. The research revealed various instances of non-compliance in post-PIPL privacy policies, especially concerning inadequate risk assessments for sensitive data. Although users identified some non-compliant activities like app eavesdropping, issues related to individual consent proved challenging. Surprisingly, over half of the interviewees believed that the government could access their personal data without explicit consent. Our findings and implications can be valuable for lawmakers, online platforms, users, and future researchers seeking to enhance personal privacy practices both in China and globally.

Content and interaction-based mapping of Reddit posts related to information security

AbstractEnsuring the privacy and safety of platform users has become a complex objective due to the emerging threats that surround any type of network, software, and hardware. Scams, malwares, hackers, and security vulnerabilities form the epicenter of cyber threats causing severe damage to the affected systems and sensitive data of users. Thus, users turn to online social networks to report cyber threats, discuss topics of their interest, and obtain knowledge concerning the various perspectives of information security. In this study, we aim to address the concepts of social interactions surrounding information security-related content by retrieving and analyzing Reddit posts from 45 relevant subreddits. In this regard, a word clustering approach is employed, based on the Affinity Propagation algorithm, that leads to the extraction and interpretation of 54 concepts. These concepts are relevant to information security and some more generic areas of interest including social media, software vendors, and labors. Furthermore, to provide a more comprehensive overview of users’ activity in the different Reddit communities/subreddits, a knowledge map associating subreddits and concepts based on their conceptual similarities is also established. The analysis shows that the descriptions of the examined subreddits are strongly related to their underlying concepts. At the same time, the outcomes also assess the conceptual associations between the different subreddits, offering knowledge related to similar and distant communities. Ultimately, two post metrics are utilized to explore how the concepts may impact user interactions. This allows us to differentiate between concepts associated with posts typically endorsed by communities, resulting in increased information exchange (via comments), or contributing as news/announcements. Overall, the findings of this study can be used as a knowledge basis in determining user interests, opinions, perspectives, and responsiveness, when it comes to cyber threats, attacks, and malicious activities. Also, the respective outcomes can contribute as a guide for identifying similar communities/subreddits and themes. Regarding the methodological contributions of this study, the proposed framework can be adapted to similar datasets and research goals as it does not depend on the special characteristics of the imported data, offering, in turn, a practical approach for future research.

Актуальные вопросы развития методики анализа финансового состояния по данным бухгалтерской отчётности

The article is devoted to the development and improvement of methods for analyzing the financial condition of organizations. As a result of reviewing and researching the content of existing methods for analyzing the financial condition of organizations, difficulties of use for users were identified, consisting of a large or small number of stages, sections, oversaturation of coefficients, lack of a common conceptual apparatus for all, incompleteness and obsolescence of recommended values of coefficients characterizing financial state, in low demand and the possibility of using the methodology for analyzing financial condition equally for all organizations. Based on the assessment and generalization of current analysis techniques, an optimal structural and logical diagram of the financial condition analysis technique has been developed, highlighting specific areas of analysis in a certain sequence of their implementation. In order to optimize the number of coefficients, a set of calculation indicators has been proposed and justified, which will allow a faster and more comprehensive assessment of the financial condition of organizations, which will increase the interest of users in the use of improved methods, as well as in the use of analysis results to improve the financial condition and increase the efficiency of management of the organization.

Transfer learning in cross-domain sequential recommendation

Sequential recommendation captures users' dynamic preferences by modeling the sequential information of their behaviors. However, most existing works only focus on users' behavior sequences in a single domain, and when there is insufficient data in the target domain, the recommendation performance may not be satisfactory. We notice that a user's interests are usually diverse, for which the items he/her interacts with in a period of time may be from multiple domains. Moreover, there are also item transition patterns across sequences from different domains, which means that a user's interaction in one domain may affect his/her interaction in the other domains next time. In this paper, we aim to improve the performance of sequential recommendation in the target domain by introducing users' behavior sequences from multiple source domains, and propose a novel solution named transfer via joint attentive preference learning (TJAPL). Specifically, we tackle the studied problem from the perspective of transfer learning and attentive preference learning (APL). For target-domain APL, we adopt the self-attention mechanism to capture the users' dynamic preferences in the target domain. Furthermore, to address the scarcity challenge posed by limited target-domain data, we introduce users' behavioral sequences in the source domain, and devise cross-domain user APL to transfer and share the users' overall preferences from multiple source domains to the target domain. We also design cross-domain local APL that specializes in capturing the item transition patterns across different domains for knowledge transfer. These modules are all based on the attention mechanism and thus can accelerate the training by parallel computation. Notice that our TJAPL can be applied to scenarios with multiple source domains, while transferring knowledge from multiple domains is potentially helpful in practical applications. Extensive empirical studies indicate that our TJAPL significantly outperforms thirteen recent and competitive baselines.

Provably secure data selective sharing scheme with cloud-based decentralized trust management systems

AbstractThe smart collection and sharing of data is an important part of cloud-based systems, since huge amounts of data are being created all the time. This feature allows users to distribute data to particular recipients, while also allowing data proprietors to selectively grant access to their data to users. Ensuring data security and privacy is a formidable task when selective data is acquired and exchanged. One potential issue that emerges is the risk that data may be transmitted by cloud servers to unauthorized users or individuals who have no interest in the particular data or user interests. The prior research lacks comprehensive solutions for balancing security, privacy, and usability in secure data selective sharing schemes inside Cloud-Based decentralized trust management systems. Motivating factors for settling this gap contain growing concerns concerning data privacy, the necessity for scalable and interoperable frameworks, and the increasing dependency on cloud services for data storage and sharing, which necessitates robust and user-friendly mechanisms for secure data management. An effective and obviously secure data selective sharing and acquisition mechanism for cloud-based systems is proposed in this work. We specifically start by important a common problematic related to the selective collection and distribution of data in cloud-based systems. To address these issues, this study proposes a Cloud-based Decentralized Trust Management System (DTMS)-connected Efficient, Provably Secure Data Selection Sharing Scheme (EPSDSS). The EPSDSS approach employs attribute-based encryption (ABE) and proxy re-encryption (PRE) to provide fine-grained access control over shared data. A decentralized trust management system provides participant dependability and accountability while mitigating the dangers of centralized trust models. The EPSDSS-PRE paradigm would allow data owners to regulate granular access while allowing users to customize data collection without disclosing their preferences. In our strategy, the EPSDSS recognizes shared data and generates short fingerprints for information that can elude detection before cloud storage. DTMS also computes user trustworthiness and improves user behaviour administration. Our research demonstrates that it’s able to deliver trustworthy and safe data sharing features in cloud-based environments, making it a viable option for enterprises seeking to protect sensitive data while maximizing collaboration and utilization of resources.