User Features Research Articles

DOGE: LLMs-Enhanced Hyper-Knowledge Graph Recommender for Multimodal Recommendation

In recent years, there has been a burgeoning interest in multimodal recommender systems within the recommendation systems domain. These systems aim to understand user preferences by leveraging both user interaction data and multimodal information associated with items. This approach frequently results in superior recommendation accuracy compared to traditional models that rely solely on user-item interactions. Despite the advancements of these methods, there is a relatively low utilization of image features in propagating item-item characteristics, an overreliance on text feature similarity, and a frequent neglect of the deep relationships between items, users, and modalities. In response to these challenges, we introduce a novel model termed LLMs-Enhanced Hyper-Knowledge Graph Recommender for Multimodal Recommendation (DOGE). DOGE utilizes large language models (LLMs) to understand image information under the guidance of text information, generating cross-modal features that effectively enhance the relationship between text and image modalities. Subsequently, DOGE constructs a Hyper-Knowledge Graph (HKG) using user-item interaction information and modality features enhanced by large language models. This graph encompasses a wide range of item-item and user-user binary relations and hyper-relations, effectively expanding the feature propagation mechanisms and mitigating the overreliance on text modality. By learning on heterogeneous user-item graphs and homogeneous item-item, user-user graphs, DOGE enhances potential effective propagation between item features and user features, acquiring more effective feature representations of users and items. Comprehensive experimentation across three public real-world datasets illustrates that DOGE attains state-of-the-art (SOTA) performance, exhibiting a 7.2% improvement over the strongest baseline.

Integrating Personalized Spatio-Temporal Clustering for Next POI Recommendation

Location-Based Social Networks (LBSNs) offer a rich dataset of user activity at Points-of-Interest (POIs), making next POI recommendation a key task. Traditional algorithms face challenges due to broad searching scopes, affecting recommendation accuracy. Users tend to visit nearby POIs and show temporal concentration in their activities, reflecting personalized spatio-temporal clustering. However, individual user data may be insufficient to capture these clustering effects for personalized recommendations. In this paper, we propose an integrated Personalized Spatio-Temporal Clustering Model (iPCM) for next POI recommendation. The model learns this kind of personalized spatio-temporal clustering effect by using global historical trajectory data in conjunction with user feature embeddings. It integrates the features of personalized spatio-temporal clustering with the user's trajectory, and completes the user's POI recommendation through a Transformer encoding and MLP decoding. To enhance the accuracy of predictions, we add a module of probability adjustment. The experimental results on multiple datasets show that with the help of personalized spatio-temporal clustering, the proposed iPCM is superior to existing methods in various evaluation metrics.

THGNets: Constrained Temporal Hypergraphs and Graph Neural Networks in Hyperbolic Space for Information Diffusion Prediction

Information diffusion prediction aims to predict the next infected user in the information diffusion, which is a critical task to understand how information spreads on social platforms. Existing methods mainly focus on the sequences or topology structure in euclidean space. However, they fail to sufficiently consider the hierarchical structure or power-law structure of the underlying topology of information cascade graphs and social networks, resulting in distortion of user features. To tackle above issue, we propose an innovative Constrained Temporal Hypergraphs and Graph Neural Networks (THGNets) framework that is tailored for information diffusion prediction. Specifically, we introduce hyperbolic temporal hypergraphs neural network to alleviate the distortion of user features by hyperbolic hierarchical learning in information cascades. Additionally, it also captures high-order dynamic interaction patterns between users and further integrates the time-consistency constraint mechanism to mitigate the instability and non-smoothness of user features in latent space. In parallel, we apply the hyperbolic graph neural network to investigate the hierarchical structure and user homogeneity on social networks, enhancing our understanding of social relationships. Moreover, hyperbolic gated recurrent units are employed to capture the potential dependency relationships between contextual users. Experiments conducted on four public datasets demonstrate that the proposed THGNets significantly outperform the existing methods, thereby validating the superiority and rationality of our approach.

Personalized Recommendation System of E-Commerce in the Digital Economy Era: Enhancing Social Connections with Graph Attention Networks

ABSTRACT In the era of digital economy, e-commerce platforms face significant challenges in providing personalized recommendations due to data sparsity, a result of limited user-product interactions. This research introduces a model that enhances e-commerce personalization by leveraging graph attention networks to mine and integrate information from user-item (U-I) score graphs and user-user (U-U) social graphs. The U-U social graph captures user relationships, providing supplementary information to uncover social connections and preferences, especially among users with sparse interactions, thereby alleviating data sparsity concerns. Additionally, the model incorporates graph contrastive learning to extract universal features of users and items, further mitigating the sparse data challenge. Our approach, when applied to the CiaoDVD and Epinions datasets, demonstrated a 1.21% improvement in Mean Absolute Error (MAE) and a 2.22% improvement in Root Mean Square Error (RMSE) on the CiaoDVD dataset, and a 1.71% improvement in MAE and a 2.11% improvement in RMSE on the Epinions dataset, outperforming all baseline methods.

Pemanfaatan Website Aljazeera Learning Arabic sebagai Media Pembelajaran Mahārah Istimā’

Arabic language learning plays an important role as an international language used by millions of people in various countries. One of the obstacles faced by beginner learners is limited access to interactive learning media, especially in developing listening skills. The gap for beginner learners, experienced by class VIII B MTs Muhammadiyah 03 Ketro, is the limited learning media. Therefore, we employ conventional media for Arabic language learning. We chose Class VIII B because the students' Arabic language comprehension skills remain at a basic level.he digital era, the use of technology is one solution to overcome various learning challenges, including increasing student interest and understanding. The purpose of this study was to introduce and determine students' responses to the Al Jazeera Learning Arabic website as a learning medium. The method used in this study is qualitative with a descriptive approach. This method was chosen because it allows researchers to obtain data that is narrated in the form of words and sentences that are certainly related to the research being conducted. The Aljazeera Learning Arabic website has three features for beginner users, namely introduction, beginners, and basics, which are also equipped with evaluations. Some feedback from students, after studying together using the Al Jazeera Learning Arabic website, shows that the use of the website is very helpful in learning mahārah istima'. In addition, students feel helped by the website's existence as a learning medium for listening skills.

User group-enhanced user feature distribution transfer framework for non-overlapping cross-domain recommendations

User group-enhanced user feature distribution transfer framework for non-overlapping cross-domain recommendations

User features and gendered patterns in repeat online advice-seeking behavior

There is limited understanding of the user-level variables related to thecommon activity of repeat advice-seeking on virtual communities. Genderis of particular interest; past studies indicate men participate less on supportforums. Social role theory provides descriptions of the interplay betweengender roles and help-seeking, but these have not been quantified in an onlinecontext. We conduct a computational linguistic analysis on a year-longdataset of 17896 users on Reddit’s r/relationships, comparing thedemographics, linguistic choices, and user histories of repeat and nonrepeat advice-seekers self-identifying as male or female. We find strongevidence of gendered behavior, with users largely behaving more similarlyto their identified gender. Female users are more likely to be repeat adviceseekers and to engage in advice-giving alongside their future adviceseeking attempts. There are otherwise no significant differences between the proportions of male and female users who return solely to seek advice.Female users are also more likely to write longer and more informally,seeking advice on domestic issues and communication. Male users are morefocused on dating and past partners. Our findings shed light on thecharacteristics of a possibly vulnerable user population and set thefoundation for future investigations on the influence of offline social roleson online help-seeking.

Social Structure-Based Methodology for User Identity Matching Across Social Networks

Cross-social-network user matching technology can identify the same individual across different social networks, enabling cross-network data integration. This allows for precise user profiling, which facilitates applications such as user recommendation and influence maximization. In such tasks, the social structure of users plays a critical role. However, due to the vast and complex nature of social network data, issues such as data sparsity and heterogeneity across net-works arise. Additionally, as the depth of network layers increases, extract-ed features tend to degrade and become homogeneous. To address these chal-lenges, this paper proposes a Social Network Structure-based User Matching (SSUM) method. The approach introduces a subgraph structure extraction mod-ule, which, combined with the original network topology, is fed into a Trans-former layer to further extract global features. This mitigates over-smoothing and over-squeezing caused by excessive layer depth, thereby obtaining vector rep-resentations of user topological structures. Subsequently, word-level vec-tors of user information and text-level vectors of user-generated content are extracted as user attribute features. These are concatenated to form the final user embed-ding representation. User matching is then transformed into a ranking problem by calculating Manhattan distances. Furthermore, to enhance model perfor-mance, user relationships in the dataset are pre-completed. Ex-perimental results on real-world social network datasets demonstrate that the proposed method effectively extracts user features, improves matching accura-cy, and outperforms comparative models.

Preferences for Mobile Apps That Aim to Modify Alcohol Use: Thematic Content Analysis of User Reviews.

Nearly one-third of adults in the United States will meet criteria for alcohol use disorder in their lifetime, yet fewer than 10% of individuals who meet for alcohol use disorder criteria will receive treatment for it. Mobile health (mHealth) applications (apps) have been suggested as a potential mechanism for closing this treatment gap, yet there is a wide variety of quality and integrity within these apps, leading to potential harms to users. The aim of this paper is to systematically record and qualitatively examine user reviews or mHealth apps to identify features in the existing apps that may impact usefulness and adoption of them. The researchers used Apple App and Google Play stores to identify mHealth apps that were focused on modifying alcohol use and treating common comorbidities. Apps that were free without in-app purchases and provided multiple features for users were included. User reviews from the apps were downloaded and coded using content analysis. A total of 425 unique apps were found in our search. Of these, the majority of apps (n=301) were excluded from the present analyses for not focusing on reducing alcohol-related concerns (eg, many apps were for purchasing alcohol). Eight apps were identified and had user reviews downloaded. The apps examined in this study were VetChange, SMART, DrinkCoach, SayingWhen, AlcoStat, Celebrate Recovery, TryDry, and Construction Industry Helpline. A total of 370 reviews were downloaded and 1353 phrases were coded from those reviews into a total of 11 codes. The 5 most common themes identified were praise (498 counts coded; 36.831%), tools (150 counts coded; 11.062%), suggestions for improvement (118 counts coded; 8.756%), criticism (105 counts coded; 7.768%), and tracking (104 counts coded; 7.724%). The current findings suggest that alcohol mobile app users broadly found the apps helpful in reducing their drinking or meeting their drinking goals. Users were able to identify features that they liked or found helpful in the apps, as well as provide concrete feedback about features that they would like included or improved. Specifically, flexible and expansive tracking features and comprehensive whole health tools were cited as valuable and desired. App developers and those looking to expand access to and uptake of alcohol reduction apps may find these user reviews helpful in guiding their app development.

Social media content and suicidality: Implications for practice.

Artificial intelligence is a useful tool for examining suicidality on social media, where people share their thoughts. However, existing research has primarily focused on text analysis to predict risk in single posts and raised privacy concerns. This study aimed to use text, images, and user features on Instagram to predict the risk of each user in Hawaii with user permission. One hundred forty-two participants completed online questionnaires about their current suicidal ideation. With their consent, their Instagram data were collected to train Artificial intelligence model to predict suicidal ideation of each user. Thirty-three (23.2%) participants reported having current suicidal ideation. The best model predicts suicidal ideation with 52% sensitivity, 92% specificity, 65% positive predictive value, and 82% accuracy. The significant predictors were negative description, lower hue and more red in an image, fewer nature and sky images, more art, fashion, a close-up of a body part, and spoof content in an image. These findings highlight the potential of suicide prediction on social media, which help mental health providers plan patient online interventions and appointments. Additionally, the influence of Hawaii's unique social-cultural context on significant predictors, helping gatekeepers to recognize signs of suicide on the social media of people in Hawaii. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

MSR: A Personalized Movie Recommendation Model Based on Gate Mechanism and Attention Network

Personalized recommendation systems play a crucial role in alleviating information overload and satisfying users’ specific preferences. To address the challenges of inadequate user historical data extraction and the cold start problem inherent in traditional movie recommendation systems, we present a novel personalized movie recommendation model known as “movie recommendation with starring roles and ratings” (MSR). By incorporating a multi-head attention mechanism, the model captures intricate relationships among diverse data fields within users’ viewing records and facilitates the extraction of user features through the basic information-rating joint attention network (BRJA). The gate mechanism efficiently integrates fundamental movie information and average score into the movie representation vector, thereby generating candidate movie features. MSR can effectively provide recommendations even when confronted with limited user information, effectively mitigating the cold start problem. Comparative experiments on the movie lens dataset and ablation experiments focusing on key modules demonstrate the effectiveness of MSR in improving movie recommendations.

Exploring incel group dynamics: a computational study of hierarchy and group-boundary policing

Incels (involuntary celibates) are part of a broader misogynistic culture known as the manosphere. Some communities within the manosphere, including incels, promote gender-based violence through misogynistic rhetoric and ideology. Incels are men who struggle to form romantic relationships and thus seek solace in online forums to find a sense of purpose and community. The community is organised around an ideology and a hierarchical classification of members. This paper presents a computational linguistic analysis of the utterances made within the community. We analyse the linguistic patterns of six different ranks of users: paragons, high-rank users, low-rank users, greycels, banned users, and self-banned/deleted users. We conducted an analysis of the sentiments expressed between ranks and of the affective attitude of posters when discussing a variety of topics. We then we analysed linguistic features, also conditioned on the rank of the poster. These analyses reveal qualitative differences between users of different ranks in how they express themselves and their sentiments. Finally, we trained models based on the linguistic features of users’ posts and show that already based on this information it is possible to predict the rank of users. Overall, our results indicate that the incel community has developed distinctive linguistic patterns to disseminate their ideology and that these linguistic patterns vary according to the rank of an incel within the community. This methodology offers a robust framework for studying similar echo chamber-like communities.

Publisher preferences for a journal transparency tool: A modified three-round Delphi study.

We propose the creation of a journal transparency tool (JTT), which will allow users to obtain information about a given scholarly journal's operations and policies. We are obtaining preferences from different stakeholders to inform the development of this tool. This study aimed to identify the publishing community's preferences for the JTT. We conducted a modified three-round Delphi survey. Representatives from publishing houses and journal publishers were recruited through purposeful and snowball sampling. The first two Delphi rounds involved an online survey with items about JTT metrics and user features. During the third round, participants discussed and voted on JTT metric items that did not reach consensus after round 2 within a virtual consensus meeting. We defined consensus as 80% agreement to include or exclude an item in the JTT. Eighty-six participants completed the round 1 survey, and 43 participants (50% of round 1) completed the round 2 survey. In both rounds, respondents voted on JTT user feature and JTT metric item preferences and answered open-ended survey questions regarding the JTT. In round 3, a total of 21 participants discussed and voted on JTT metric items that did not reach consensus after round 2 during an online consensus group meeting. Fifteen out of 30 JTT metric items and none of the four JTT user feature items reached the 80% consensus threshold after all rounds of voting. Analysis of the round 3 online consensus group transcript resulted in two themes: 'factors impacting support for JTT metrics' and 'suggestions for user clarity.' Participants suggested that the publishing community's primary concerns for a JTT are to ensure that the tool is relevant, user-friendly, accessible, and equitable. The outcomes of this research will contribute to developing and refining the tool in accordance with publishing preferences.

Disentangled Multi-Graph Convolution for Cross-Domain Recommendation

Data sparsity poses a significant challenge for recommendation systems, prompting the research of cross-domain recommendation ( CDR ). CDR aims to leverage more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, a major challenge in CDR is the identification of transferable features. Traditional CDR methods struggle to distinguish between the various features of users, including domain-invariant features that are effective for feature transfer and domain-specific features that are detrimental to cross-domain information transfer. In this paper, we aim to disentangle domain-invariant features and domain-specific features and effectively utilize these different features. This enables effective domain-to-domain information transfer by only transferring domain-invariant features while still considering the role of domain-specific features within their respective domains. Based on the superiority of graph structural feature learning and disentangled represent learning, we propose \(\mathbf{DMGCDR}\) —a model that learns D isentangled user feature representations and constructs a M ulti- G raph network for bidirectional knowledge transfer of shared features for CDR . Specifically, we designed two regularization terms to disentangle domain-invariant features and domain-specific features. Subsequently, we established a multi-graph convolutional network to enhance domain-specific features within single-domain graphs and transfer domain-invariant features across cross-domain graphs. Our approach also includes designing feature constraints to enhance the combination of features derived from different graphs and to uncover potential correlations among them. Extensive experiments on real-world datasets have demonstrated that our model significantly outperforms state-of-the-art cross-domain recommendation approaches.

The Graph Attention Recommendation Method for Enhancing User Features Based on Knowledge Graphs

Knowledge graphs have shown great potential in alleviating the data sparsity problem in recommendation systems. However, existing graph-attention-based recommendation methods primarily focus on user–item–entity interactions, overlooking potential relationships between users while introducing noisy entities and redundant high-order information. To address these challenges, this paper proposes a graph-attention-based recommendation method that enhances user features using knowledge graphs (KGAEUF). This method models user relationships through collaborative propagation, links entities via similar user entities, and filters highly relevant entities from both user–entity and user–relation perspectives to reduce noise interference. In multi-layer propagation, a distance-aware weight allocation mechanism is introduced to optimize high-order information aggregation. Experimental results demonstrate that KGAEUF outperforms existing methods on AUC and F1 metrics on the Last.FM and Book-Crossing datasets, validating the model’s effectiveness.

C2lRec: Causal Contrastive Learning for User Cold-start Recommendation with Social Variable

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending to new users, known as the user cold-start problem. Some approaches incorporate social networks to deduce preferences based on the social circles of cold-start users to solve the problem of sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in social behaviors, leading to inaccuracies in predicting user preferences. To address the aforementioned issues, we propose the Causal Contrastive Learning Recommendation (C2lRec) framework. Specifically, we causally model the inference of hidden preferences from the feature and historical behavior of warm users and predict user interactions based on such preferences. The counterfactual inference is subsequently performed to intervene and extract interactions from historical behaviors of warm users that influence their preferences, designating as primary causal variables. Additionally, we utilize the primary causal variables from users within the social circle of cold-start users to substitute the missing historical interactions of cold-start users and employ a similar causal modeling approach to uncover hidden preferences as we do with warm users. Finally, we realize causal contrastive learning to enhance the distribution of cold-start users. Extensive experiments conducted on three public datasets demonstrate that the recommendation performance of C2lRec exceeds that of state-of-the-art methods.

From genies performing magic to sages imparting wisdom: a value-centred survey of music AI user interfaces, creative affordances and artist objectives

There are, at present, multiple fronts of debate about the ethics of Commercial Creative AI (ComCAI) product development. The issues of copyright and the right to know the AI provenance of work are dominant and well-defined. Meanwhile, a significant area of debate is trickier to define and analyse, relating to ComCAI's impact on the intrinsic values and virtues of human creative endeavour. In this paper, I analyse this impact by surveying the emerging landscape of user interfaces (UIs) in the domain of commercial music AI products. I do this through a value-centred design lens, considering how it is possible to articulate intrinsic values in the arts. Specifically, I select two artistic objectives: the need for creative artists to create works that are differentiated from others; the need for deep cognitive creative engagement with the work being created. I ask how ComCAI UIs can enable these artistic objectives, first through a survey of UI types and their combination in products, second through user feature requests. This design analysis tentatively suggests that those ComCAI tools promising to support artists increasingly need to provide greater control, in order to satisfy their creative users, and this is focussed towards either simply allowing post-generation editing, or improving generative control, which is in tension with the opaque nature of the generative systems. I propose that a value-centred design approach that highlights values such as differentiation and creative engagement helps to identify under-explored design spaces which have the potential to make better music AI experiences.

Exploring the role of sentiment analysis with network and temporal features for finding influential users in social media platforms

Exploring the role of sentiment analysis with network and temporal features for finding influential users in social media platforms

Personalized Analysis of Online Civic Education in Colleges and Universities with Improved Recommendation Algorithm

Abstract Civic education is a research hotspot in recent years, and the current development of Civic education in colleges and universities mainly suffers from the lack of course Civic case reference. Therefore, this paper systematically designs and develops a recommendation algorithm for Civic and Political Education resources. A collaborative filtering recommendation algorithm based on the popularity correction factor is proposed, which reduces the weight of popular items in the algorithmic calculation process, so as to recommend some of the Civic and Political courses that users are interested in in a more personalized way. Then the appropriate values of the weighting factors for the above improved algorithm are determined and fused. To improve the accuracy and personalization of the recommendation, a weighted hybrid recommendation algorithm integrating user feature interaction and collaborative filtering is proposed. The algorithm integrates various behavioral features of users to obtain a hybrid recommendation algorithm based on the weighted fusion of user features and collaborative filtering. After experimental comparison, it is proved that the performance index of the improved algorithm in this paper is significantly improved compared with other recommendation algorithms, and the average time of recommendation is reduced by 0.1 s. Applying the recommendation algorithm to personalized teaching, the Civics education exam scores of the experimental class after personalized learning are higher than the average scores of the control class by 8.95 points, and there is a significant difference between the two groups’ exam scores after the experiment, and the students and Civic education class-related learning behavior performance. Education classroom-related learning behavior performance has improved. It shows that the improved recommendation algorithm in this paper meets the personalized needs of students’ Civics course learning and achieves better recommendations.

Research on the Construction of Smart Education Platform Driven by Artificial Intelligence Technology in the New Era Higher Education Self-study Examination

Abstract In the context of the new era, the rapid development of Internet technology, artificial intelligence and other technologies, the field of education will enter the “era of intelligent education”. The research centers on the self-study examination of higher education, and builds an artificial intelligence-driven intelligent teaching platform, which integrates the functions of intelligent management, intelligent examination, intelligent education, intelligent learning, intelligent evaluation, intelligent service and so on. A feature extraction module is proposed based on convolutional neural network and knowledge graph, which utilizes the powerful feature extraction capability of CNN to extract the implicit features of courses and users, constructs a course resource recommendation model, and analyzes the application of artificial intelligence in the smart teaching platform. The accuracy (0.857 and 0.791) and area under the ROC curve (0.876 and 0.804) results of the model on different datasets are better than those of the comparison model, reflecting better resource recommendation performance. After using the smart education platform, more than 57% of learners have positive attitudes towards the platform, their learning experience, and their learning outcomes. The smart education platform constructed in the article can enhance learners’ interest, motivation, performance, and efficiency in learning, and help them achieve intelligence and efficiency in self-study exam preparation.