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Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest. [ABSTRACT FROM AUTHOR]

This paper reports on a study on delinquent acts in three academic libraries in northern Ghana. A survey method was used for the study and a questionnaire was used to collect data. The study population consisted of staff in the Reader Service Sections of the three libraries. The study found that failure to observe library regulations, keeping overdue materials, eating in the library, littering, reserving seats unjustifiably, and hiding of books for the purpose of monopoly are examples of delinquent acts practiced by library users. According to the participants, the main reasons for delinquent acts are inadequate orientation to users, insufficient copies of books, and indiscipline among library users. The respondents further reported that these actions led to a reduction in library resources and low patronage. To curb deviant behaviors in libraries, the study recommends the adoption of pragmatic approaches including massive orientation for users, imbibing positive library culture that fosters respect, bolstering commitment and cooperation among library users and the staff, increasing library budgets as well as finding more creative ways of acquiring more copies of books, such as using Open Educational Resources (OERs), belonging to consortia and building and/or enhancing existing Institutional Repositories (IR). [ABSTRACT FROM AUTHOR]

Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users' social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users' increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users' topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are. [ABSTRACT FROM AUTHOR]

The internet has become inundated with vast amounts of information, making it increasingly difficult for users to find precise and reliable content in their respective domains. Recommendation algorithms have emerged as a solution to this problem, enabling personalized content recommendations based on user preferences. One domain that greatly benefits from recommendation systems is music, with its diverse genres and countless songs. In this research paper, we present a novel approach to music recommendation that leverages user likes, plays, ratings, and other factors to generate personalized playlists. The proposed hybrid recommendation system analyses songs using six features and incorporates new features such as ELLT and DVS designed for dimensionality reduction to enhance accuracy. It continuously adapts to evolving user preferences and music trends, making personalised recommendations using cosine similarity and song weights. We test our approach through extensive experimentation and achieve promising precision, recall, and F1 scores. This contribution advances personalized content recommendation and hybrid recommendation systems, demonstrating their potential to improve music listening experiences. [ABSTRACT FROM AUTHOR]

Filter pruning is a compression and acceleration method for deep convolutional neural network models that operates at a large scale. Many researchers have studied this approach and made significant progress, but the problem remains an open research topic. In this paper, we propose a DCNN structure optimization method for filter order–information structure similarity graph clustering with global pruning rate control, which considers the mapping strength distribution of convolution kernels in filters and the influence of equivalent convolution kernels on filter similarity. In this method, the relative strengths of the mappings between different convolution kernels in a filter determine the overall type of information combined in the output channel through the superposition filtering of information extracted from different input channels. The structural differences between the equivalent convolution kernels of different filters reflect the differences between the types of information extracted from the same input channel in the convolutional layer. By combining these two factors, we construct a measure of filter order–information structure similarity and then construct a filter similarity graph for the convolutional layer. For the pruning strategy, we establish a convolutional layer filter number allocation model with global pruning rate control using the scaling factors of batch normalization (BN) layers in sparse network. Then, in the filter similarity graph, we perform filter pruning by clustering subgraphs according to the given filter number allocation model for each convolutional layer. This yields an optimized structure for the pruned DCNN model. The experimental results and analysis demonstrate that our proposed method achieves effective pruning. In particular, on the ImageNet dataset, when pruning ResNet-50, the acceleration ratio and compression ratio of the model are 5.31x and 3.78x, respectively, while the model's classification accuracy decreases only slightly. Our method outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study introduces an innovative hybrid approach for personalized trip recommendations, aiming to enhance existing recommender systems by leveraging multidimensional data. Our proposed method integrates user preferences and diverse contextual factors to address challenges related to data sparsity effectively. To overcome this hurdle, our methodology employs a clustering approach, streamlining the extraction of Points of Interest (PoI) and reducing computational complexity. The framework comprises three key components: I) a unique strategy for context assessment, achieved by combining contextual information in vector form through the Term-Frequency-Inverse-Document-Frequency technique, II) the incorporation of tourist demographic information to alleviate the Cold Start problem, and III) the implementation of an asymmetric schema that elevates the traditional similarity paradigm. Moreover, our approach utilizes personalized PoIs in consecutive travel patterns, enabling the retrieval and ranking of an optimal list of potential routes. The experimental results based on Flickr and Yelp datasets reveal that the proposed method surpasses prior work on all three metrics, achieving a significant 8% increase in precision and an 11% increase in F-Score, thereby enhancing the quality metrics of personalized trip recommendations. [ABSTRACT FROM AUTHOR]

Summary: Existing research on recommender systems primarily focuses on improving a single objective, such as prediction accuracy, often ignoring other crucial aspects of recommendation performance such as temporal factor, user satisfaction, and acceptance. To solve this problem, we proposed an explicable recommendation model using many‐objective optimization and a time‐assisted knowledge graph, which utilizes user interaction times within the graph to prioritize recommending recently frequently visited items and is further optimized using a many‐objective optimization algorithm. In this model, the temporal weight of user actions at different times is first determined through a time decay function. Additionally, if a user clicks on the same item again, the current action's temporal weight is set to one. This strategy prioritizes recent user actions and frequently visited items, reflecting current interests and preferences better. Next, the created knowledge graph is used to create a list of potential recommendations. Embedding methods obtain the vectors for entities and relations in the path. These vectors, combined with the temporal weight of actions, quantify the explainability of user recommendations. Optimizing the rest of the recommendation performance with many objective algorithms while focusing on the user's recent frequent visits to the item. Finally, the outcomes of the research study indicate that, compared to other explicable recommended methods, our model, considering temporal factor, improved average accuracy by 11%, diversity by 1%, and explainability by 21% in the Useraction1 data set. Results in other data sets also indicate that the proposed model maintains accuracy, diversity, and novelty while enhancing explainability. [ABSTRACT FROM AUTHOR]

Summary: Matrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods. [ABSTRACT FROM AUTHOR]

The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS. [ABSTRACT FROM AUTHOR]

It is widely accepted that there is an urgent need to make green hydrogen (GH2) projects financially viable to reduce global warming. However, any form of improvements to these GH2 projects lead to substantial cost increase. Due to this cost increase, making many improvements negatively affects the financial profitability of hydrogen projects. This is why there is a need for new advanced financial priority analysis tools so that it is easier to develop GH2 projects globally. Accordingly, the aim of this study is to identify and then define the most important factors affecting GH2 generation projects. To achieve this aim, this work proposes a new fuzzy multi-criteria decision-making model based on artificial intelligence (AI). First, experts are weighted with AI technique. Second, the missing evaluations are filled via a recommender system. Third, criteria weights are calculated by the M-SWARA technique integrated with quantum picture fuzzy rough (QPFR) sets. Finally, GH2 energy generation processes are listed by the QPFR-VIKOR approach. Overall, the main contribution of this study is the generation of a comprehensive AI oriented fuzzy decision-making model to make a detailed evaluation with respect to the financial potential improvements of the GH2 generation projects. The main originality of this model is the consideration of AI to calculate the weights of the criteria. Similarly, another benefit of the proposed model, that increases its superiority against other models, is the completion of missing evaluations by experts thanks to the recommender system. It is concluded that the most important criterion affecting green hydrogen investments is organizational effectiveness. • The most important factors of green hydrogen projects are defined. • The importance weights of experts are calculated with the AI technique. • M-SWARA is used to compute the criteria weights. • QPFR-VIKOR is considered to rank alternatives. • The most important criterion is organizational effectiveness. [ABSTRACT FROM AUTHOR]

Accelerated by technological advancements, while online platforms equipped with recommendation algorithms offer convenience to obtain information, it also brought algorithm bias, shaping the norms and behaviors of their users. The filter bubble, conceived as a negative consequence of algorithm bias, means the reduction of the diversity of users' information consumption, garnering extensive attention. Previous research on filter bubbles typically used users' self-reported or behavioral data independently. However, existing studies have disputed whether filter bubbles exist on the platform, possibly owing to variations in measurement methods. In our study, we took content category diversity to measure the filter bubbles and innovatively used a combination of participants' self-reported and website behavioral data, examining filter bubbles on a single online video platform (Bilibili). We conducted a questionnaire survey among 337 college students and collected 3,22,324 browsing records with their informed authorization, constituting the dataset for research analysis. The existence of filter bubbles on Bilibli is found, such that diversity will decrease when viewing Game videos increases. Furthermore, we considered the factors that influence filter bubbles from the perspective of demographics and user behavior. In demographics, female and non-member users are more likely to be trapped in filter bubbles. In user behavior, results of feature importance analysis indicate that the diversity of information consumption of heavy users is higher than others, and both activity and fragmentation have an impact on the formation of filter bubbles, but in different directions. Finally, we discuss the reasons for these results and a theoretical explanation that the filter bubbles effect may be lower than we thought for both heavy and normal users on online platforms. Our conclusions provide valuable insights for understanding filter bubbles and platform management. [ABSTRACT FROM AUTHOR]

This study consolidates the existing literature and develops a conceptual framework for a holistic understanding of the tourist experience with AI-enabled technologies. It entails performance analysis of scientific actors, thematic analysis, and content analysis of the intellectual structure of the research. The key technologies shaping the tourist experience include service robots, personalized recommender systems, IoT, chatbots, AR/VR, big data analytics, text mining, text analytics, and NLP. The study contributes by offering performance and science mapping in the field of AI for the tourist experience. Accordingly, several future research directions and a consolidated conceptual framework are proposed. [ABSTRACT FROM AUTHOR]

Conversational recommendation systems are crucial for making recommendations agreeable to the user. To reach an agreeable recommendation, this study proposes a dialog strategy that represents a reasonable order of items and elicits the current estimation by the wording of utterances based on the subjective preference estimation. We developed two dialog functions for the topic and word choice based on the history of preference estimations. The human impression of a robot's diligence, understanding capability, and satisfaction were evaluated through a conversation with a virtual robot using a crowdsourcing platform. We compared six conditions that differed based on two topics and three wording patterns. The experimental results indicated that the main effect of the wording patterns, whereas one of the topic choices was not found to be significant. Further analysis showed that accurate estimation improves the robot's impression when demonstrating its diligence. [ABSTRACT FROM AUTHOR]

AbstractPeople use diverse read-it-later tools to save webpages for future uses; however, difficulties in scrap retrieval remain. Our study aims to understand users’ mental models of read-it-later tools and suggests a new tool, Read-It-Now, for web scraping. We conducted interviews to identify web scraping habits and developed four main design recommendations for read-it-later tools: tool hierarchy, contextual aids, proactive triggers, and multiple navigation strategies. With a focus on the proactive trigger that facilitates scrap retrieval, Read-It-Now is designed to recommend relevant web scraps based on a comparison of the title of the current browser tab. We tested the design prototype against traditional bookmarks and found that users preferred Read-It-Now because of its simplicity and effectiveness; however, explanations for scrap recommendations could be improved. [ABSTRACT FROM AUTHOR]

Session-based recommendation systems (SRS) can predict the next action of anonymous users based on their historical behavior sequence, while some features directly influence whether an interaction occurs, which leads to inaccurate recommendation results that can’t reflect the user’s real preferences. This causes confounding effects in SRS, which causes the recommendation system to misunderstand user preferences and recommend unsatisfied items to users. To address this problem, we propose a session-based recommendation framework via counterfactual inference and attention networks (SRS-CIAN). This framework introduces external attention mechanisms into session recommendation tasks and combines causal graphs for modeling while capturing information on items within the session. We use counterfactual inference to refactor counterfactual scenarios for handling the confounding effects. Through extensive empirical experiments on real-world datasets, we demonstrate that our approach surpasses several strong baselines for confounding effects. [ABSTRACT FROM AUTHOR]

Recommender systems have been a kind of powerful tool to improve e-commerce benefits. Existing recommender systems mostly employ explicit features of products, such as description and attributes. However, visual characteristics contain fruitful implicit and intuitive information, and are always ignored by existing works. To deal with this issue, this paper proposes a novel intelligent recommender system via visual Transformer (ViT) model and convolutional neural network (CNN) structure. First, the ViT part is utilized to extract visual feature representation. By learning the visual similarity among products, it is expected to obtain a better scene understanding. Then, an improved CNN part is utilized to extract hidden association information from historical behaviors of users. It is expected to better perceive user preference and purchasing characteristics. The combination of two parts constructs the proposed recommender system. Finally, we make performance evaluation for the proposal on real-world e-commerce dataset. The results indicate that the proposal exhibits high recommendation accuracy and efficiency. Compared with other typical algorithms, our proposal can better understand product images and user behaviors, providing more personalized recommendation results. [ABSTRACT FROM AUTHOR]

Tourism recommendation systems have tended to become popular in recent years. Due to the fact that tourism content is generally with the format of multimodal information, existing research works mostly ignored the fusion of various feature types. To deal with this issue, this paper resorts to multimodal fusion of semantic analysis and image comprehension, and proposes a novel deep learning-based recommender system for tourism routes. First, semantic analysis under tourism route search is conducted, in order to complete destination selection and process selection. Then, image comprehension of overall tourism route planning is conducted by establishing an end-to-end object recognition model. Finally, the previous two parts of characteristics are fused together to formulate an integrated recommender system with multimodal sensing ability. This thought is expected to bring a stronger ability for tourism route discovery. Empirically, operational efficiency and stability analysis are carried out on real-world data to evaluate the performance of the proposal. The experimental results show that it can achieve significant improvement in tourism route recommendation, can accurately capture user preferences, and can provide travel suggestions that meet user requirements. [ABSTRACT FROM AUTHOR]

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold (⁠|$\rm {\mathit{n}_{s}}$|⁠), to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes |$\rm {\mathit{P}_{mean}}$| and |$\rm {IC_{mean}}$| parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores (⁠|$\rm {\mathit{P}_{mean}}$|⁠), filtering based on Information Content (⁠|$\rm {IC_{mean}}$|⁠), and segmentation score (⁠|$\rm {n_{s}}$|⁠). Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data. [ABSTRACT FROM AUTHOR]

Sequential Recommendation systems play a crucial role in predicting users' preferences based on their behavioral history. However, the existing methods ignore the extrapolation nature of sequences and do not make deep use of item provider information. This oversight limits the model's ability to fully utilize relevant external properties. To alleviate these limitations, we design a recommendation model that incorporates Position encoding and external reinforcement (Item -Provider), named DPSRec. Specifically, we design an Embed Encoding layer, in order to distinguish the Position Embedding of previous sequence models, we combine the time variability with the position encoding with extrapolation property, and encode the item and item provider sequences accordingly. Meanwhile, to avoid the noise that the initial item embeddings might cause with Position Encoding, we calculate the position Encoding separately from the item embedding. In addition, we design a Cross Propagation layer to capture implicit higher-order dependencies between item sequences. Extensive experiments on three real-world datasets demonstrate that the proposed model generally outperforms the baselines by about 1–12.5%. Our source code will be published after the paper is published. [ABSTRACT FROM AUTHOR]

ABSTRACT Introduction Aim Implications for Practice EUPD (a personality disorder, characterised by emotion dysregulation, impulsivity and unstable relationships) is a controversial diagnosis affecting around 1 in 100 people, often times for a large part of their life, causing immense suffering to the individual and their families and friends.As someone diagnosed with EUPD, I offer a lived experience perspective on internalised stigma building on growing attention to the impact of prejudice and discrimination faced when navigating the healthcare system with this diagnosis.Include a recommendation to go ‘back to basics’ and focus on us (especially when accessing crisis care) feeling heard, seen, validated, empowered and understood. [ABSTRACT FROM AUTHOR]

This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals. [ABSTRACT FROM AUTHOR]

The sharing of social media data has provided a significant amount of information about users. This has led to a growing interest in predicting user personality from social network data. Predicting user personality has potential applications in advertising and content recommendation systems, and can be a valuable source of insights for professionals seeking to understand and target their audiences on social media. This study focuses on the Instagram application and aims to extract and analyze the personality of Instagram users based on the content of their photos. Three categories of features are extracted from images: visual, emotional, and content. To describe users' personalities, we used the Big Five model. One of the main contributions of this study is the creation of a comprehensive database that includes 316 Moroccan Instagram users. This database is the first of its kind to belong to Moroccan users. Additionally, The study also analyzed the influence of personality traits and gender on photo content by independently assessing the personalities of female and male Instagram users. Moreover, the personalities of each gender were extracted separately, and the influence of the number of images on prediction accuracy was studied. The study evaluates prediction accuracy using the root mean square error (RMSE) on a [1,5] score scale. Commendable results were achieved across all personality traits, with standout performance in predicting conscientiousness for females (RMSE = 0.59) and openness for males (RMSE = 0.59) compared to previous studies. [ABSTRACT FROM AUTHOR]

Recommendation Systems have obtained huge attention with notion to assist users in determining their interests by prognosticating their ratings or preferences on specific item. Concurrently, the unique capability of RL (Reinforcement Learning) agent to learn from environment for reward without training the data makes it specifically suitable approach for such systems. Due to such ability, traditional works have considered DRL (Deep RL) for recommendation system. However, existing studies faced several challenges like scalability issues, probability for overlapping of numerous values and information loss while passing into a NN (Neural Network) and improper model training which lead to incorrect recommendations. Hence, this study intends to resolve these existing pitfalls. To accomplish this, the research proposes a DRR (DRL based Recommendation) framework in accordance with actor-critic learning. In actor network, DWL-FA (Deep Weighted Likelihood-Factor Analysis) is proposed for modifying the existing DNN (Deep Neural Network) to new-environment for compensating vector through the removal of unwanted regions in network results. Attention mechanism considered in this process affords decoder with suitable information from each hidden-states of the encoder. This attention mechanism along with DWL-FA model is further capable of selectively concentrating on valuable input sequences thereby effectively learning association amongst them. This assists the trained model to learn better. Subsequently, in critic network, HMP-WU (Hidden Markov Probability-Weight Updation) is proposed for optimizing the interactions amongst users with their preferences for the recommended items (environment) and recommender system (agent). In this case, weight updation process assists in comprehending related sequences thereby resolving incorrect predictions. These proposed processes have made the system explore better results with an increase of 5.74% with regard to average of p-value. [ABSTRACT FROM AUTHOR]

Diversity plays a major role when a student is looking for a tutor to better understand some concepts or an entire course. In fact, algorithms for recommending potential tutors have to take into account several aspects of diversity that may be critical to successful tutoring. The tutor must have the appropriate competencies in the subject matter. In addition, he/she must be able to convey the knowledge and skills associated with the tutoring topic. Moreover, the personality traits of the tutee and the tutor can facilitate or hinder the learning process during tutoring. This study presents the experience of ‘SOS TUTORÍA UC’, a responsive web application aimed at facilitating academic assistance among students. Special emphasis is placed on the importance of incorporating dimensions of diversity that can inform the artificial intelligence algorithms of the potential tutor recommendation system. Indeed, competence in the topic tutored is the first diversity criterion for recommending more knowledgeable potential tutors. In addition, the tutee has to choose whether to look for tutors with personalities that are “different”, “similar” or “indifferent” to his or her own preferences for academic assistance on the specific topic. To achieve this, ‘SOS TUTORÍA UC’ is integrated with the WeNet platform, which provides user management services and user recommendation algorithms. The results of the testing of the recommendation system were positive with regard to the criterion of competence, while the criterion of personality should be addressed for improvement. In order to improve the tutor-tutee matching process, participants emphasized the importance of considering the criterion of personality traits in addition to competence. They also requested additional information and parameters to facilitate tutor selection. [ABSTRACT FROM AUTHOR]

With the wide application of the intelligent news algorithm in the news industry, its recommendation mechanism has become the primary way for news consumers to obtain information. This study explores the intelligent news algorithm recommendation mechanism’s risk management measures and optimization schemes. Thus, people can get transparent news information more efficiently. Firstly, the study classifies and analyzes the risks of information filtering bias, information cocoon effect, and information bubble in intelligent news algorithm recommendation mechanism and collects and introduces large-scale news data as a data source. Secondly, the intelligent news algorithm recommendation model based on the convolutional neural network is constructed. The model uses word embedding technology to transform news articles into vector representations and trains the model to learn the feature representations of news articles and the correlation between them. Moreover, the loss function and weight of the model are adjusted to improve the diversity and balance of the recommendation results. Finally, simulation experiments are carried out to evaluate the model’s performance. The results reveal that the information diversity of the system model in this study is increased by 15%, and user satisfaction and the information quality index are increased by 10% and 7%. It proves the importance of diversified data sources, algorithm transparency and explainability, user feedback and participation, and balanced recommendation strategies to reduce risk and improve the performance of recommendation mechanisms. Therefore, the research results guide the practical application of the intelligent news algorithm recommendation mechanism and provide a reference for further improvement and optimization of the recommendation algorithm. [ABSTRACT FROM AUTHOR]

Educational dashboards are increasingly prevalent visualising technologies that display data from educational processes to help learners and educators track learning pathways, alert for deviations, and make interventions. This study contributes to critical studies on data visualisations in education by examining dashboards in higher distance education. Inspired by science and technology studies (STS), it investigates several modes of relating to educational dashboards at one distance university, focusing on their production and deployment. Ethnographic findings show how dashboards are produced through public-private, interdisciplinary collaboration and (made to) align with predominant techno-pedagogical ideas, often by persuading university students to use technologies correctly. When deployed, students learn to filter relevant information, practice self-monitoring, and (re)examine dashboard usage. The case exemplifies a ‘dashboarding of learning’ and ‘learning to dashboard’, indicating that dashboards not only enter educational practices but also encourage actors – sometimes unsuccessfully – to understand and realise their education in line with specific techno-pedagogical ideas. [ABSTRACT FROM AUTHOR]

Finding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders' conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system. [ABSTRACT FROM AUTHOR]

Given pairwise comparisons between multiple items, how to rank them so that the ranking matches the observations? This problem, known as rank aggregation, has found many applications in sports, recommendation systems and other web applications. We focus on the ranking problem under the Erdös–Rényi outliers model: only a subset of pairwise comparisons is observed, being either clean or corrupted copies of the true score differences. We investigate the spectral ranking algorithms that are based on unnormalized and normalized data matrices. The key is to understand their performance in recovering the underlying scores of each item from the observed data. This reduces to deriving an entry-wise perturbation error bound between the top eigenvectors of the unnormalized/normalized data matrix and its population counterpart. By using the leave-one-out technique, we provide a sharper |$\ell _{\infty }$| -norm perturbation bound of the eigenvectors and derive an error bound on the maximum displacement for each item, with only |$O(n\log n)$| samples. In addition, we also derive the sample complexity to perform top- |$K$| ranking under mild assumptions. Our theoretical analysis improves upon the state-of-the-art results in terms of sample complexity, and our numerical experiments confirm these theoretical findings. [ABSTRACT FROM AUTHOR]

The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations. Our model incorporates a double-tower network (DT-Net) that learns user and item representations through a meta-encoder and a mutual attention encoder, effectively mitigating the impact of noisy data on auxiliary information. By framing the cold-start problem as few-shot meta-learning, we employ a model-agnostic meta-optimization strategy to train the model across a variety of tasks during the meta-learning phase. Extensive experiments conducted on three real-world datasets—ShortVideos, MovieLens, and Book-Crossing—demonstrate the superiority of our model in cold-start recommendation scenarios. Compared to MetaCs-DNN, the second-best approach, CFSM, achieves improvements of 1.55%, 1.34%, and 2.42% under the AUC metric on the three datasets, respectively. [ABSTRACT FROM AUTHOR]

Many exceptional deep learning networks have demonstrated remarkable proficiency in general object detection tasks. However, the challenge of detecting ships in synthetic aperture radar (SAR) imagery increases due to the complex and various nature of these scenes. Moreover, sophisticated large-scale models necessitate substantial computational resources and hardware expenses. To address these issues, a new framework is proposed called a stepwise attention-guided multiscale feature fusion network (SAFN). Specifically, we introduce a stepwise attention mechanism designed to selectively emphasize relevant information and filter out irrelevant details of objects in a step-by-step manner. Firstly, a novel LGA-FasterNet is proposed, which incorporates a lightweight backbone FasterNet with lightweight global attention (LGA) to realize expressive feature extraction while reducing the model's parameters. To effectively mitigate the impact of scale and complex background variations, a deformable attention bidirectional fusion network (DA-BFNet) is proposed, which introduces a novel deformable location attention (DLA) block and a novel deformable recognition attention (DRA) block, strategically integrating through bidirectional connections to achieve enhanced features fusion. Finally, we have substantiated the robustness of the new framework through extensive testing on the publicly accessible SAR datasets, HRSID and SSDD. The experimental outcomes demonstrate the competitive performance of our approach, showing a significant enhancement in ship detection accuracy compared to some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a two-stage Bayesian persuasion model in which a third-party platform controls the information available to the sender about users' preferences. We aim to characterize the optimal information disclosure policy of the platform, which maximizes average user utility, under the assumption that the sender also follows its own optimal policy. We show that this problem can be reduced to a model of market segmentation, in which probabilities are mapped into valuations. We then introduce a repeated variation of the persuasion platform problem in which myopic users arrive sequentially. In this setting, the platform controls the sender's information about users and maintains a reputation for the sender, punishing it if it fails to act truthfully on a certain subset of signals. We provide a characterization of the optimal platform policy in the reputation-based setting, which is then used to simplify the optimization problem of the platform. [ABSTRACT FROM AUTHOR]

To address the issue of user preference drift and capture dynamic user preferences more accurately in sequence recommendation, this paper proposed a novel model named SILSSRec. The model initially leveraged categories and frequencies of items as side information to generate personalized user embeddings based on users' historical interaction sequences. Then it created personalized temporal interval embeddings by considering the time intervals between historical and current interactions, and integrated these embeddings with item feature embeddings to form personalized temporal embeddings. The model employed attention mechanisms and gated recurrent networks to extract users' long-term and short-term preferences from the embedding representations. Furthermore, it used contrastive learning to reinforce the feature representation of preferences, and an adaptive aggregation network dynamically combined these two types of preferences to form the final preference representation of users. Experiments on eight public datasets demonstrate that SILSSRec outperforms existing baseline models on evaluation metrics, with an average increase of 3.82% in AUC, 7.2% in recall rate, and 0.3% in precision. The results validate that SILSSRec performs well in various scenarios, effectively mitigating the preference drift issue and enhancing recommendation performance. [ABSTRACT FROM AUTHOR]

Heterogeneous graph embedding models based on non-contrastive learning (NCL) do not rely on negative sampling to learn the intrinsic features and patterns, which may cause the model fail to efficiently learn the differences between vertexes. This paper proposed a heterogeneous graph embedding model based on cross-view prototype non-contrastive learning (XPNCL), which learnt better node representations for downstream tasks by finding additional positive samples with more contextual information, and reconsidered the similarity between positive samples. The model firstly designed a tree structure based on random walks in heterogeneous graph. This directed filtering tree (DFT) about positive samples contained rich neighboring and semantic information by filtering out random walk paths that satisfied local structural constraints. Secondly, to achieve the alignment of similar samples in terms of numerical and quantitative from multiple dimensions, XP-NCL defined the cross-view prototype index (ISDR) and peak operator based on the characteristics of heterogeneous graphs. Furthermore, the model trained using stop-gradient updating. Finally, experiments verify the classification and clustering performance of the node on ACM, DBLP and freebase datasets, and the results show that even without the negative samples, the XP-NCL representation can achieve superior performance in many cases compared to other homogeneous and heterogeneous graph baselines. [ABSTRACT FROM AUTHOR]

Knowledge reasoning is a fundamental task in knowledge graph completion. It is a popular topic in the academie community. Integrating reinforcement learning and knowledge reasoning is a viable solution to improve the inference effectiveness and interpretability of models. Taking the problem of knowledge graph research as a path or sequence decision problem in the knowledge reasoning method based on reinforcement learning can make better use of semantic information, such as entities and relationships, to improve reasoning effect and interpretability. This paper provided a descriptive overview of the basic concepts of knowledge graph and knowledge reasoning, and described the research progress in recent years. The paper analysed and compared the related research on knowledge reasoning based on reinforcement learning from two perspectives; single-layer and double-layer reinforcement learning knowledge reasoning. Furthermore, the paper explored the application of knowledge reasoning in various domains, including knowledge question answering, intelligent recommendation systems, healthcare, and transportation. Lastly, the paper discussed future research tendencies for reinforcement learning-based knowledge reasoning and offered insights into potential avenues for exploration and development. [ABSTRACT FROM AUTHOR]

TikTok has emerged as a powerful platform for the dissemination of mis- and disinformation about the war in Ukraine. During the initial three months after the Russian invasion in February 2022, videos under the hashtag #Ukraine garnered 36.9 billion views, with individual videos scaling up to 88 million views. Beyond the traditional methods of spreading misleading information through images and text, the medium of sound has emerged as a novel, platform-specific audiovisual technique. Our analysis distinguishes various war-related sounds utilized by both Ukraine and Russia and classifies them into a mis- and disinformation typology. We use computational propaganda features—automation, scalability, and anonymity—to explore how TikTok's auditory practices are exploited to exacerbate information disorders in the context of ongoing war events. These practices include reusing sounds for coordinated campaigns, creating audio meme templates for rapid amplification and distribution, and deleting the original sounds to conceal the orchestrators' identities. We conclude that TikTok's recommendation system (the "for you" page) acts as a sound space where exposure is strategically navigated through users' intervention, enabling semi-automated "soft" propaganda to thrive by leveraging its audio features. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) represents a pivotal shift towards sustainable mobility, yet the challenge of efficient charging station recommendations persists, influencing user convenience and EV uptake. This study introduces a novel approach utilizing Q-learning for simulating EV charging station recommendations, aiming to optimize the matching process between EVs and charging infrastructure. By integrating Markov decision processes with Q-learning algorithms, we dynamically adapt recommendations to user behaviours and preferences, significantly enhancing recommendation accuracy and personalization. The methodology involves constructing a simulation environment to model EV charging behaviour, evaluating the performance of the Q-learning based recommendation system under various scenarios. Results demonstrate the effectiveness of this approach in identifying optimal charging strategies, thus contributing to improved user satisfaction and charging station utilization. The findings underscore the importance of innovative technological integration for addressing the complexities of sustainable urban mobility. [ABSTRACT FROM AUTHOR]

Selecting the right academic major significantly shapes an individual's future career path, making it a longstanding focus of research. The shift to online platforms, accelerated by the challenges posed by the coronavirus pandemic, has transformed counseling and guidance systems. Consequently, developing robust online support systems has become imperative for extending guidance to all students. This article introduces the design, development, and evaluation of "My Future Career," a multidimensional recommendation system (RS) crafted to aid students in navigating university and academic major selection decisions. The system relies on three key student‐driven parameters: central university entrance exam scores, rankings, and occupational personality types, utilizing cosine similarity and normalized distance to align user and item profiles. Following the system's completion, an assessment was conducted using data from real users, revealing an impressive accuracy (hit rate 100%, precision 88%) in recommendations following the inclusion of contextual post‐filtering features. The findings not only highlight the system's effectiveness but also underscore the positive user experience, as students express contentment with its ease of use and practical utility. The results emphasize the endorsement of expert's regarding the system's consistency (52%), relevance (96%), and acceptance (96%) in providing recommendations. Practitioner Points: Due to the pivotal role of academic major selection in shaping future careers, there is a highlighted need to assist students in making informed decisions.This article introduces a multidimensional recommendation system designed to aid students in making informed decisions about their academic major selections.The results highlight the system's impressive accuracy (100% hit rate, 88% precision), positive user feedback, and expert endorsement regarding both relevance and acceptance. [ABSTRACT FROM AUTHOR]

The Remote Sensing Image Change Captioning (RSICC) has recently emerged in the field of remote sensing image interpretation; it aims to automatically predict natural language captions of significant semantic changes in bi-temporal remote sensing images. Recent studies of RSICC have improved the accuracy of change captions of bi-temporal remote sensing images to a large extent. Nevertheless, there still remain challenges in multi-scale perception of ground objects and feature enhancement of bi-temporal remote sensing images. To address these challenges and further improve the accuracy of RSICC, a novel deep learning–based end-to-end scale-wised feature enhancement network (SFEN) is proposed in this paper. SFEN integrates four efficient blocks: 1) the siamese backbone network (SBN) to extract initial features of bi-temporal remote sensing images, 2) the siamese receptive field fusion (SRFF) block to explicitly capture multi-scale semantic information of ground objects in bi-temporal feature maps, 3) the siamese global feature enhancement (SGFE) block to adaptively enhance key information and filtering redundant features of bi-temporal feature maps in both channel and spatial dimensions, 4) the change caption decoder (CCD) to map bi-temporal feature maps into natural language. The SFEN aims to precisely capture significant semantic information of ground objects in bi-temporal remote sensing images and predict accurate change captions. Experimental results on LEVIR-CC dataset demonstrate our SFEN outperforms recent state-of-the-art (SOTA) approach in RSICC by 5.2% on CIDEr-D and achieves a new SOTA. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation's land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of "Spider" plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield. [ABSTRACT FROM AUTHOR]

Purpose: This study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment. Design/methodology/approach: We proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used. Findings: The effect of AI-generated misinformation on people's perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users' heuristics or is validated by the diagnosticity they perceive. Research limitations/implications: When exposed to misinformation through algorithmic recommendations, users' perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation. Practical implications: Perceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people's perceptions of misinformation and AI features is an efficient way to change their misinformation behavior. Social implications: Although there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users' cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread. Originality/value: The mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167 [ABSTRACT FROM AUTHOR]

To enhance the precision of the music recommendation environment system, a novel design approach has been introduced, utilizing multi-label propagation and hierarchical clustering analysis for a dual music recommendation environment. First of all, the process model of music recommendation environmental system is built based on music recognition system, which is composed of music signal preprocessing module, music model, sound model and music recognizer; second, on the basis of further study on the clustering validity, a new clustering validity function is established by describing the intra-class compactness and inter-class separation of clustering through fuzzy similarity relation; finally, the validity of the proposed music double recommendation environmental system using multi-label propagation hierarchical clustering analysis is verified by simulation experiment. The results show that the recommendation method based on comprehensive evaluation of user characteristics is suitable for single-category users, while the recommendation method based on multi-category evaluation is suitable for multi-category users. This approach offers an effective and precise means to enhance the accuracy and customization of music recommendation systems, thereby increasing user satisfaction. [ABSTRACT FROM AUTHOR]