1. The Internet as Cognitive Enhancement - PMC - NCBI
6 apr 2020 · Similarly, Buchanan argues that computers and the Internet are some of the best technologies for cognitive enhancement because they open the ...
The Internet has been identified in human enhancement scholarship as a powerful cognitive enhancement technology. It offers instant access to almost any type of information, along with the ability to share that information with others. The aim of ...
2. [PDF] A recommendation for a new Internet-based environment ... - ERIC
Secondly, depending on a literature review, the authors propose a new Internet-based learning. Erdogan Kartal, Ph.D., assistant professor, Department of ...
3. Recommender systems and the amplification of extremist content
30 jun 2021 · We provide a novel empirical analysis of three platforms' recommendation systems when interacting with far-right content.
Recommendation algorithms potentially amplifying extremist content has become a policy concern in recent years. We conduct a novel empirical experiment on three platforms (YouTube, Reddit, and Gab) to test this phenomenon. We find that YouTube’s “Recommended for you” system does promote extreme content. We synthesise the findings into the policy debate and argue that co-regulation may provide some solutions.
4. A similarity-enhanced hybrid group recommendation approach in ...
Therefore, we propose a similarity-enhanced hybrid group recommendation approach named HGRA for cloud manufacturing. Specifically, we implement the HGRA system ...
With the development of cloud manufacturing (CMfg), a huge amount of services appears on the Internet, which makes recommender systems in CMfg service a promising research field. To this end, recent studies mainly focus on solving individual recommendation to meet the requirements of every user. However, due to the time complexity problem, ‘Many-to-Many’ recommendation mode is increasing in real applications. To implement such a group recommendation is very challenging, because the system not only needs to achieve high recommendation quality but also satisfies user clusters in an even way. Therefore, we propose a similarity-enhanced hybrid group recommendation approach named HGRA for cloud manufacturing. Specifically, we implement the HGRA system by three main components. Firstly, an enhanced user similarity measuring approach is designed to identify a similar user group based on the non-linear model Proximity-Significance-Singularity (PSS) and Kullback-Leibler (KL) distance algorithms. Secondly, a set of user subgroups are further clustered using K-medoids algorithm, in which additional information similarity is calculated by making full use of functional information about the users. Thirdly, a weighted ranking aggregation model is established to generate a recommendation list according to the representative user of each subgroup. The performance of our system is tested by two data sets from real-world cloud manufacturing systems. The experimental results demonstrate the fea...
5. Internet of Things is a revolutionary approach for future technology ...
9 dec 2019 · Internet of Things is a revolutionary approach for future technology enhancement: a review. Sachin Kumar ORCID: orcid.org/0000-0003-3949-0302 ...
Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.
6. [2403.14377] Knowledge-Enhanced Recommendation with User-Centric ...
21 mrt 2024 · Computer Science > Information Retrieval · Title:Knowledge-Enhanced Recommendation with User-Centric Subgraph Network · Bibliographic and Citation ...
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-item interaction matrices has limitations, especially in scenarios where there is a lack of interaction data for new items. Knowledge graph (KG)-based recommendation systems have emerged as a promising solution. However, most KG-based methods adopt node embeddings, which do not provide personalized recommendations for different users and cannot generalize well to the new items. To address these limitations, we propose Knowledge-enhanced User-Centric subgraph Network (KUCNet), a subgraph learning approach with graph neural network (GNN) for effective recommendation. KUCNet constructs a U-I subgraph for each user-item pair that captures both the historical information of user-item interactions and the side information provided in KG. An attention-based GNN is designed to encode the U-I subgraphs for recommendation. Considering efficiency, the pruned user-centric computation graph is further introduced such that multiple U-I subgraphs can be simultaneously computed and that the size can be pruned by Personalized PageRank. Our proposed method achieves accurate, efficient, and interpretable recommendations especially for new items. Experimental results demonstrate the superiority of KUCNet over state-of-the-art KG-based and collaborative filtering (CF)-based methods.
7. EQUATOR Network | Enhancing the QUAlity and Transparency Of ...
find reporting guidelines | improve your writing | join our courses | run your own training course | enhance your peer review | implement guidelines. Library ...
The EQUATOR Network executive group have recently published a position statement on data sharing reporting. The statement sets out the EQUATOR Network’s support for data sharing practices and the importance of reporting data management and sharing plans.
8. Topic-aware Intention Network for Explainable Recommendation with ...
Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement. Authors: Qiming Li. Qiming Li. Institute of Computing Technology ...
Recently, recommender systems based on knowledge graphs (KGs) have become a popular research direction. Graph neural network (GNN) is the key technology of KG-based recommendation systems. However,...
9. Reviews Meet Graphs: Enhancing User and Item Representations for ...
In this paper, we propose a neural recommendation approach which can utilize useful information from both review content and user-item graphs.
Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
10. An Adversarial Generation Method for Robust Augmentation in Sequential ...
24 mrt 2024 · Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation. Authors. Junyang Chen Shenzhen ...
Sequential Recommendation plays a significant role in daily recommendation systems, such as e-commerce platforms like Amazon and Taobao. However, even with the advent of large models, these platforms often face sparse issues in the historical browsing records of individual users due to new users joining or the introduction of new products. As a result, existing sequence recommendation algorithms may not perform well. To address this, sequence-based data augmentation methods have garnered attention. Existing sequence enhancement methods typically rely on augmenting existing data, employing techniques like cropping, masking prediction, random reordering, and random replacement of the original sequence. While these methods have shown improvements, they often overlook the exploration of the deep embedding space of the sequence. To tackle these challenges, we propose a Sparse Enhanced Network (SparseEnNet), which is a robust adversarial generation method. SparseEnNet aims to fully explore the hidden space in sequence recommendation, generating more robust enhanced items. Additionally, we adopt an adversarial generation method, allowing the model to differentiate between data augmentation categories and achieve better prediction performance for the next item in the sequence. Experiments have demonstrated that our method achieves a remarkable 4-14% improvement over existing methods when evaluated on the real-world datasets. (https://github.com/junyachen/SparseEnNet)
11. [PDF] ROBUST RECOMMENDATION VIA SOCIAL NETWORK ENHANCED ...
Abstract: Robust product recommendation enables internet ... author: Fei Jiang, Department of Epidemiology & Biostatistics, University of California,.
12. The Advantage of the 5G Network for Enhancing the Internet of Things ...
Author to whom correspondence should be addressed. Sensors 2024, 24(8) ... [12] is a detailed review of IoT data-network infrastructure that explores ...
The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network.
13. Hypergrah-Enhanced Dual Convolutional Network for Bundle ...
18 dec 2023 · View a PDF of the paper titled Hypergrah-Enhanced Dual Convolutional Network for Bundle Recommendation, by Kangbo Liu and 4 other authors.
Bundle recommendations strive to offer users a set of items as a package named bundle, enhancing convenience and contributing to the seller's revenue. While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles. This compromise can result in information loss, ultimately impacting the overall model performance. To address this gap, we develop a unified model for bundle recommendation, termed hypergraph-enhanced dual convolutional neural network (HED). Our approach is characterized by two key aspects. Firstly, we construct a complete hypergraph to capture interaction dynamics among users, items, and bundles. Secondly, we incorporate U-B interaction information to enhance the information representation derived from users and bundle embedding vectors. Extensive experimental results on the Youshu and Netease datasets have demonstrated that HED surpasses state-of-the-art baselines, proving its effectiveness. In addition, various ablation studies and sensitivity analyses revealed the working mechanism and proved our effectiveness. Codes and datasets are available at https://github.com/AAI-Lab/HED
14. Product recommendation using enhanced convolutional neural ...
Product recommendation using enhanced convolutional neural network for e-commerce platform. Authors: Yarasu Madhavi Latha. Yarasu Madhavi Latha. Department of ...
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15. A feature-enhanced knowledge graph neural network for machine ... - PeerJ
28 aug 2024 · The authors have chosen to make the review history of this article public. Abstract. Large amounts of machine learning methods with condensed ...
Large amounts of machine learning methods with condensed names bring great challenges for researchers to select a suitable approach for a target dataset in the area of academic research. Although the graph neural networks based on the knowledge graph have been proven helpful in recommending a machine learning method for a given dataset, the issues of inadequate entity representation and over-smoothing of embeddings still need to be addressed. This article proposes a recommendation framework that integrates the feature-enhanced graph neural network and an anti-smoothing aggregation network. In the proposed framework, in addition to utilizing the textual description information of the target entities, each node is enhanced through its neighborhood information before participating in the higher-order propagation process. In addition, an anti-smoothing aggregation network is designed to reduce the influence of central nodes in each information aggregation by an exponential decay function. Extensive experiments on the public dataset demonstrate that the proposed approach exhibits substantial advantages over the strong baselines in recommendation tasks.
16. Journal of Medical Internet Research
Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to a different ...
Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet
17. Internet Research - Emerald Insight
Important note for authors: phishing scams. Close ... Rethinking privacy in the Internet of Things: a comprehensive review of consumer studies and theories.
Internet Research available volumes and issues
18. sentiment analysis and collaborative filtering from Twitter social network ...
In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of ...
Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines sentiment analysis of learners’ reviews on social networks with collaborative filtering methods to provide more personalized and relevant course recommendations. To achieve this, we explored different models to analyze the sentiments of tweets related to online courses. Additionally, we used collaborative filtering based on k-nearest neighbors (KNN). Our results demonstrate that integrating sentiment analysis provides more relevant recommendations. This has also been shown based on the calculation of root mean square error (RMSE) compared to a traditional approach. In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of their course recommendations, tailoring them to each individual learner’s needs.
