User Relevance Research Articles

Improving Web Readability Using Video Content: A Relevance-Based Approach

With the increasing integration of multimedia elements into webpages, videos have emerged as a popular medium for enhancing user engagement and knowledge retention. However, irrelevant or poorly placed videos can hinder readability and distract users from the core content of a webpage. This paper proposes a novel approach leveraging natural language processing (NLP) techniques to assess the relevance of video content on educational websites, thereby enhancing readability and user engagement. By using a cosine similarity-based relevance scoring method, we measured the alignment between video transcripts and webpage text, aiming to improve the user’s comprehension of complex topics presented on educational platforms. Our results demonstrated a strong correlation between automated relevance scores and user ratings, with an improvement of over 35% in relevance alignment. The methodology was evaluated across 50 educational websites representing diverse subjects, including science, mathematics, and language learning. We conducted a two-phase evaluation process: an automated scoring phase using cosine similarity, followed by a user study with 100 participants who rated the relevance of videos to webpage content. The findings support the significance of integrating NLP-driven video relevance assessments for enhanced readability on educational websites, highlighting the potential for broader applications in e-learning.

GNNRI: detecting anomalous social network users through heterogeneous information networks and user relevance exploration

GNNRI: detecting anomalous social network users through heterogeneous information networks and user relevance exploration

SCBG: Semantic-Constrained Bidirectional Generation for Emotional Support Conversation

The Emotional Support Conversation (ESC) task aims to deliver consolation, encouragement, and advice to individuals undergoing emotional distress, thereby assisting them in overcoming difficulties. In the context of emotional support dialogue systems, it is of utmost importance to generate user-relevant and diverse responses. However, previous methods failed to take into account these crucial aspects, resulting in a tendency to produce universal and safe responses (e.g., “I do not know” and “I am sorry to hear that”). To tackle this challenge, a semantic-constrained bidirectional generation (SCBG) framework is utilized for generating more diverse and user-relevant responses. Specifically, we commence by selecting keywords that encapsulate the ongoing dialogue topics based on the context. Subsequently, a bidirectional generator generates responses incorporating these keywords. Two distinct methodologies, namely, statistics-based and prompt-based methods, are employed for keyword extraction. Experimental results on the ESConv dataset demonstrate that the proposed SCBG framework improves response diversity and user relevance while ensuring response quality.

O conceito de relevância na recuperação de informação

This work aims to present a bibliographical survey on the concept of relevance, its properties and characteristics, as well as systematizing its manifestations and its use in Information Science, particularly in the process of information retrieval. To achieve this, the research was based on a bibliographical survey of several authors who have relevance as an object of study. It found that the concept of relevance has several formal definitions and can be interpreted in different ways. Relevance is also strongly linked to information retrieval systems. In this context, it can be classified under two approaches: user relevance and system relevance. From the results, we conclude that relevance plays a fundamental role in Information Science, with emphasis on the information retrieval process, which often uses this concept as a metric to evaluate the effectiveness of algorithms and systems.

The Integration of Lingnan Water Culture in Red Dance Art in the Context of Big Data

Abstract This paper realizes the digital analysis, display, and sharing of urban culture by constructing a digital platform of cultural resources in Lingnan and collecting and indexing its cultural resources. Through collaborative filtering, a recommendation algorithm is used to focus on dance resources and analyze Lingnan water culture through the red dance function. The realization process takes the user information recommendation function as an example, and its overall process is resource loading and data acquisition, user relevance calculation, user information recommendation calculation, data collection, and writing into the database. The results show that among the 14 red dance works, such as “The Golden Hairpin”, “Brothers,” and “The Yellow River”, 10 works and 11 works have incorporated the elements of the water culture of Lingnan Province into their music and costumes, respectively. From 9 times used in the first edition of the competition to 24 times in the fourth edition, it shows that the red dances pay more and more attention to the integration of the water culture elements of Lingnan. The number of South Anhui water culture elements used in the first session (P=0.038) was significantly smaller than that in the fourth session (P=0.03). Gradually, the utilization of both outstanding works (from 0.658 to 0.902) and rookie works increased from the first to the fourth session. The content of the integration of Lingnan water culture into red dance should be expanded in the future, and only about 35% of the students had a supportive attitude, which should be continued in the future.

AdaML: An Adaptive Meta-Learning model based on user relevance for user cold-start recommendation

User cold-start recommendation is a critical problem due to the sparse user–item​ interactions of new users. To address this issue, an emerging popular direction employs the meta-learning paradigm, which can quickly adapt to new tasks with sparsely labeled data. Early meta-learning recommendation works use the same global prior to initialize the parameters for each user’s preference learning task, which may be insufficient to discern users’ complex behaviors and leads to poor generalization. As a result, recent works have proposed the concept of relevance-based meta-learning recommendation, which ensures that similar priors are locally shared by related users with similar preferences, and have yielded promising results. However, as an important factor influencing the performance of relevance-based meta-learning recommendation, related user information is still not sufficiently explored and exploited in existing works. To that end, in this paper, we present a novel adaptive meta-learning model based on user relevance (AdaML) to further improve the user cold-start recommendations. Specifically, AdaML firstly proposes a related user recognition component, which recognizes related users with similar preferences by integrating the meta-path-based and the clustering-based strategies. The retrieved high-quality information of related users is then effectively utilized by a well-designed user relevance enhanced recommender, on one hand, to enhance user representations and tailor relevance-specific priors from the globally shared prior, on the other. Finally, extensive experiments on several public datasets demonstrate AdaML’s superior performance over state-of-the-art approaches in the user cold-start scenario.

The impact of different age-friendly smart home interface styles on the interaction behavior of elderly users.

Smart homes create a beneficial environment for the lives of elderly people and enhance the quality of their home lives. This study aims to explore the design of age-friendly interfaces that can meet the emotional needs of self-care elderly people from the perspective of functional realization of the operating interface. Sixteen elderly users aged fifty-five and above were selected as subjects with healthy eyes and no excessive drooping eyelids to obscure them. Four representative age-friendly applications with different interface designs were selected from the Android application market as the stimulus material for the experiment, and a total of fifteen pages were browsed independently. During the experiment, the ErgoLAB human-computer environment synchronization cloud platform was used to monitor and record the subjects' multidimensional physiological indicators of eye movements, skin electricity, and heart rate variability when using different styles of mobile application interfaces. Combined with the post-experimental PAD emotion scale data, the preferences of emotional interface design elements of the self-care elderly were analyzed to guide the subsequent design practice. The results show that: on the layout characteristics of the home page, a simple and intuitive multi-column layout or card layout combined with a bottom navigation bar type main navigation is used; on the choice of the main color, a single color with low saturation is used as the main color, with high saturation color accents to play a role in highlighting key information; on the information density of the home page, a moderate proportion of graphics and text is adopted, with low information density; on the arrangement of the page content, they try to give priority to information content with high user relevance.

Development of accelerated reliability test cycle for electric drive system based on vehicle operating data

For the current traditional vehicle, reliability test specification is difficult to effectively cover the actual service load intensity of the electric drive system (EDS), the existing specification has the problems of insufficient evaluation of extreme operating conditions, the inaccurate definition of the whole life cycle damage target, and weak user relevance. Therefore, this paper proposes a method to construct an accelerated reliability test cycle for EDS based on 300 users' one-year operation data. By extracting the running fragments and constructing the characteristic parameters associated with the dominant failure load of the EDS. Principal component analysis and clustering algorithms identify the running fragments and obtain five typical operating conditions. Based on the unit damage intensity distribution model, the accelerated testing condition for simultaneous assessment of multiple components is selected, and each component's whole life cycle damage target is determined. To further consider the loading sequence of the fragments, a state transfer probability matrix for each condition is established based on the Markov chain model, which implements the condition combination. Using the multi-objective optimization algorithm determines the number of cycles for each condition and eventually compiles the accelerated reliability test cycle of the EDS. In addition, by conducting rig tests to verify the validity of the accelerated test load spectrum and determine the accelerated factor by the testing data. This study provides specific references and technical support for developing and validating the EDS.

User-Characteristic-Oriented Bilateral Matching between Online Learning Service Demanders and Providers

Under the background of information technology and the Internet era, the matching problem in different application scenarios is becoming increasingly prominent. With respect to the matching problem in knowledge services, enabling users to choose suitable knowledge out of massive information has become an urgent demand to be satisfied. Initiating from interdisciplinary perspective, this paper proposes a matching method for online learning services according to user characteristics, which focuses on the matching of decision making for knowledge service with user relevance and characteristic under social network environment. Firstly, the complex network among users is constructed, and the user group is subcategorized into subgroups, thereby aggregating the subgroup information effectively. Secondly, the weight of the indices that evaluate the matching subject is determined by conducting the best-worst method. Thirdly, considering the difference between the expectation and actual levels of the matching subject, the cumulative prospect theory is adopted to calculate the satisfaction degree of both sides. Aiming at maximizing the satisfaction degree of the subjects, a multi-objective optimization model is established to obtain the optimal matching pairs. Finally, the validity and rationality of the proposed method are verified, offering interdisciplinary perspective and theoretical foundation for knowledge service matching and the education reform of humanities.

CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs

Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.

Review on EEG-BCI classification techniques advancements

A BCI is a system of hardware and software integrated as an interface between the brain and the computer. A BCI translates the EEG signals, originating from the brain, into computer commands, commands that help the user interact with the external real world in a useful manner. The possibility that EEG signals can translated into any open-ended computer command, opened endless possibility. What a person can do with EEG-based BCI is now just limited by imagination. This paper will discuss advances in the practical aspects of different classifiers for EEG-based BCI, as well as the theoretical advances in signal processing and user relevance of these advance in EEG-based BCI in real-time applications.

A scalable sub-graph regularization for efficient content based image retrieval with long-term relevance feedback enhancement

The goal of content-based image retrieval (CBIR) is to search relevant images through the analysis of image content. Manifold Ranking (MR) and Efficient Manifold Ranking (EMR) method has been successfully applied to content-based image retrieval due to its ability to discover underlying geometrical structure of dataset given the query data. But given the image database is scalable, the graph in MR and EMR cannot be extended or updated as their graph size is fixed. In this paper, to solve the above problem, we consider to formulate a sub-graph based on fixed anchors, instead of constructing the graph based on the while dataset, where the anchors are selected by utilizing conventional k-means method and the sub-graph weight matrix is defined by the similarity between any pair-wise anchors. Since the number of anchors is much smaller than the original dataset, updating the sub-graph is much easier than the original graph of whole dataset. Motivated by such sub-graph construction, we then develop an efficient graph regularization framework to predict the ranking scores for the whole data along the sub-graph, where the ranking score is first propagated from query image to the partial anchors, then from partial anchors to all anchors via the sub-graph and finally to the whole dataset. It can also utilize user relevance feedbacks to update the sub-graph so that the discriminative information can be involved to enhance the retrieval performance in a long term. Extensive simulations verify the effectiveness of the proposed method.

Measuring Driver Perception: Combining Eye-Tracking and Automated Road Scene Perception.

ObjectiveTo investigate how well gaze behavior can indicate driver awareness of individual road users when related to the vehicle’s road scene perception.BackgroundAn appropriate method is required to identify how driver gaze reveals awareness of other road users.MethodWe developed a recognition-based method for labeling of driver situation awareness (SA) in a vehicle with road-scene perception and eye tracking. Thirteen drivers performed 91 left turns on complex urban intersections and identified images of encountered road users among distractor images.ResultsDrivers fixated within 2° for 72.8% of relevant and 27.8% of irrelevant road users and were able to recognize 36.1% of the relevant and 19.4% of irrelevant road users one min after leaving the intersection. Gaze behavior could predict road user relevance but not the outcome of the recognition task. Unexpectedly, 18% of road users observed beyond 10° were recognized.ConclusionsDespite suboptimal psychometric properties leading to low recognition rates, our recognition task could identify awareness of individual road users during left turn maneuvers. Perception occurred at gaze angles well beyond 2°, which means that fixation locations are insufficient for awareness monitoring.ApplicationFindings can be used in driver attention and awareness modelling, and design of gaze-based driver support systems.

Analyzing the relationship between relevance and extremist discourse in an alt-right network on Twitter

Nowadays, Twitter is used by several political extremist groups to establish close communities on which the opinions are amplified following an echo-chamber effect. However, few literature analyses the effect of the use of an extremist discourse in relation to the relevance of these users on their online network. With the aim of analyzing this effect, this work studies the relationship between the use of indicators of extremist discourse from users belonging to an alt-right network on Twitter and their relevance on it. The network of alt-right users is created using the retweets of 96 accounts where the user relevance is measured by five different types of centrality metrics, including in-degree, eigenvector, k-shells, betweenness, and closeness. Both the linguistic indicators and the tone were analyzed using LIWC and VADER software. The network analysis outcomes show that user relevance on the network is indeed related to the use of an extremist discourse. Finally, this relationship is also tested on different corpus of texts and about different topics, being found that this relationship is more clear on retweets made by the users and when discussing about hate speech topics.

Collaboratively augmented UIP – Filtered RIP with relevancy mapping for personalization of web search

Personalized information retrieval has become more crucial and challenging in today’s time, as every user is demanding information according to their own perspectives. To achieve personalization, this paper proposes a methodology with a focus on the selection of an effective composition corresponding to various supporting modules of a personalization methodology. Collaborative tagging can be quite helpful in constructing User Interest Profile (UIP) and Resource Illustration Profile (RIP). The proposed methodology also focuses on UIP augmentation using multiple strategies; and a novel approach has also been designed to handle outlier tags which caused ambiguity in collective RIP. Even a good UIP and RIP alone cannot create an efficient personalization methodology; they also require a suitable mapping with user’s query requirement. Therefore, in the proposed methodology, the fuzzy satisfaction requirement-based novel mapping functions have been designed to measure query relevance score and user interest relevance score for a web resource. These scores have been further used to calculate the post-relevance score of a web resource after a suitable trade-off. Experiments using the del.icio.us dataset show that the proposed methodology has outperformed each and every baseline by a considerable margin.

Twitter alloy steel disambiguation and user relevance via one-class and two-class news titles classifiers

This paper addresses the nontrivial task of Twitter financial disambiguation (TFD), which is relevant to filter financial domain tweets (e.g., alloy steel or coffee prices) when no unique identifiers (e.g., cashtags) are adopted. To automate TFD, we propose a transfer learning approach that uses freely labeled news titles to train diverse one-class and two-class classification methods. These include different text handling transforms, adaptations of statistical measures and modern machine learning methods, including support vector machines (SVM), deep autoencoders and multilayer perceptrons. As a case study, we analyzed the domain of alloy steel prices, collecting a recent Twitter dataset. Overall, the best results were achieved by a two-class SVM fed with TFD statistical measures and topic model features, obtaining an 80% and 71% discrimination level when tested with 11,081 and 3000 manually labeled tweets. The best one-class performance (78% and 69% for the same test tweets) was obtained by a term frequency-inverse document frequency classifier (TF-IDFC). These models were further used to generate a Financial User Relevance rank (FUR) score, aiming to filter relevant users. The SVM and TF-IDFC FUR models obtained a predictive user discrimination level of 80% and 75% when tested with a manually labeled test sample of 418 users. These results confirm the proposed joint TFD-FUR approach as a valuable tool for the selection of Twitter texts and users for financial social media analytics (e.g., sentiment analysis, detection of influential users).

How Do People Make Relevance Judgment of Scientific Data?

Many efforts have been made to explore user relevance judgment for documents, images, web pages and music in the field of information retrial. However, there is a lack of attention to scientific data even when scientists and researchers are facing an increasing data deluge. In this study, we carried out a two-phase (first exploratory and then empirical) research to explore relevance judgment patterns of scientific data users. In the exploratory study, we interviewed 23 subjects who participated in a national competition related to scientific data. Five relevance criteria (RC) and seven paths of their usage were identified by content analysis of the transcribed records of the interview. Based on the results of the first phase, seven hypotheses were proposed and verified by partial least squares structural equation modelling (PLS-SEM). The study identified five RC, i.e. topicality, accessibility, authority, quality and usefulness used by scientific data users. Three patterns were identified including 1) data topicality judgment as the first step or starting point, 2) data reliability judgment as the necessary process and 3) data utility judgment as final purpose. These findings provide new understanding of relevance judgement and behaviours of scientific data users, and could benefit the design for cognitive retrieval systems and algorithms specific to scientific data.

Research-evidence-based health policy formulation in Malawi

PurposeThe paper seeks to report on research-evidence-based health policy formulation in Malawi based on interviews with policymakers and questionnaire administered to health researchers.Design/methodology/approachQuantitative data for inferential statistical analysis was obtained through a questionnaire administered to researchers in the University of Malawi's College of Medicine and the Kamuzu College of Nursing. Interviews were conducted with four directors holding decision-making national health policy roles in the Ministry of Health and the National Assembly. The five national policymakers interviewed constituted five of the nine interviewees. The remaining four interviewed represented other government agencies and non-governmental organisations in the health sector. These constituted a piloted group of health policymakers in Malawi. Data from interviews shows illustrative comments typical of consistent perspectives among interviewees. Where they disagreed, divergent views have been presented.FindingsThe survey has revealed that health researchers rarely interact with health policymakers. Policymakers rarely attend researchers' workshops, seminars and conferences. Researchers prefer to interact with policymakers through expert committees or technical working groups. However, the meetings are called by policymakers at their own will. In terms of health research designed for user relevance, survey respondents suggested that developing research products; formulating study objectives; analysing and interpreting research findings and; developing research designs and methods were their responsibility. However, policymakers felt that research evidence should appeal to specific priorities needed by health policymakers in policy formulation. Health researchers suggested that health research evidence should be communicated through syntheses of the research literature and reprints of articles published in scientific journals. However, policymakers were of the view that research products should not be bulky, should be presented in points form and should provide options for specific policy areas.Practical implicationsUniversity research groups and technical working groups provide an opportunity for interacting and enhancing the use of health research evidence.Originality/valueFor the purposes of facilitating the use of research evidence into policy, the study provides a low-cost framework for linking research groups and technical working groups to inform health research utilisation.

Measuring user relevance in online debates through an argumentative model

Online debating forums are important social media for people to voice their opinions and engage in debates with each other. Measuring user relevance on these forums can be useful to identify different user profiles or behaviors in online debates, for example, users that tend to participate at the beginning of a debate and whose comments trigger participation, or users that post relevant comments but are not replied too much. To help users to distinguish such different user profiles, we propose graded measures based on users’ influence, the controversy that they generate throughout the debates, their contribution to the polarization of the debates, and their social acceptance, that we extract by analyzing the debates in which the users participate. Our approach is based on an argumentation-based analysis that represents a debate as a valued argumentation framework, in which comments of a debate are arguments, the attack relation between arguments models disagreement between comments, and values for arguments represent the overall support of users for comments. Finally, we test our measures with a sample of users from Reddit debates, identifying four main groups of users, from users with almost no impact on the debate to very active ones with decisive comments for the outcome of the debate.

Operator implementation of Result Set Dependent KWS scoring functions

A popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through detailed empirical and conceptual analysis studies, that the Labrador scoring function provides the best user relevance among contemporary Candidate Network scoring functions.Efficiently incorporating the Labrador function, however, is rendered difficult due to its Result Set Dependent (RSD) characteristic, wherein the distribution of keywords in the query results influences the ranking. To address this RSD challenge ►We investigate two mechanisms ►(a) a simple wrapper approach that leverages existing RDBMS functionalities through an SQL wrapper ►And (b) a more sophisticated operator approach wherein the database engine is augmented with custom operators that perform result ranking in the query execution plan.The above strategies have been implemented on a PostgreSQL codebase, inclusive of integration with the optimizer for the operator approach. A detailed empirical study over real-world data sets, including DBLP and Wikipedia, indicates that the wrapper approach addresses the RSD efficiency issue to a limited extent only. More encouragingly, the operator approach is extremely successful, delivering processing times that are comparable to, or better than, those of non-RSD implementations. We expect these results to aid in the organic hosting of KWS functionality on database systems.