scholarly journals Combining Content Information with an Item-Based Collaborative Filter

Aletheia ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Daryl Bagley
Keyword(s):  
Collaborative Filter ◽  
Content Information

Applications of Scanning Microscopy in Biomedical Research

1968 ◽  
Vol 26 ◽  
pp. 354-355
Author(s):  
T. L. Hayes
Keyword(s):  
Information Content ◽  
Biomedical Research ◽  
Biomedical Applications ◽  
Three Dimensional ◽  
Dental Enamel ◽  
Resolving Power ◽  
Whole Mount ◽  
Two Parameters ◽  
Content Information ◽  
Sectioned Tissue

Biomedical applications of the scanning electron microscope (SEM) have increased in number quite rapidly over the last several years. Studies have been made of cells, whole mount tissue, sectioned tissue, particles, human chromosomes, microorganisms, dental enamel and skeletal material. Many of the advantages of using this instrument for such investigations come from its ability to produce images that are high in information content. Information about the chemical make-up of the specimen, its electrical properties and its three dimensional architecture all may be represented in such images. Since the biological system is distinctive in its chemistry and often spatially scaled to the resolving power of the SEM, these images are particularly useful in biomedical research.In any form of microscopy there are two parameters that together determine the usefulness of the image. One parameter is the size of the volume being studied or resolving power of the instrument and the other is the amount of information about this volume that is displayed in the image. Both parameters are important in describing the performance of a microscope. The light microscope image, for example, is rich in information content (chemical, spatial, living specimen, etc.) but is very limited in resolving power.

ANALISIS VISUAL PADA SAMPUL DEPAN MAJALAH DESAIN GRAFIS “CONCEPT” EDISI ULTAH PERDANA (Sebuah Kajian Semiotika)

DeKaVe ◽  
2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Prayanto WH
Keyword(s):  
Graphic Design ◽  
Roland Barthes ◽  
Specific Design ◽  
Design Work ◽  
Design Elements ◽  
Semiotic Approach ◽  
Short Time ◽  
Content Information ◽  
Magazine Cover

Magazine is one of the forms of mass media that has fungsikomunikasi to convey information to mass audiences. The cover is an important element because it is through cover / cover one can guess the contents of the magazine, as well as further interested to know further information contained therein. On a magazine cover consists of drawings and writings are arranged in such a way that looks interesting and has meaning Press publications, especially magazines, today's not enough just to rely on the quality of news or manuscript, although verbal aspect is very important. It must be recognized that the visual aspects (design) as the cover / envelope has crucial role to capture the prospective reader. For the cover of a magazine is a window that shows the content information, can be either a text or photographs, illustrations, and design elements. The function of a magazine cover is to attract, dazzle prospective readers, by way influence the thoughts flow in a short time. So it's no wonder much current the magazine publisher who made the cover of such a way as to attract the attention of prospective readers. Thus the task of designers to magazine cover to create designs that attract the attention of the reader becomes increasingly severe. This study tries to analyze a visual on the front cover Magazine Graphic Design 'Concept' birthday inaugural edition by using the Roland Barthes' semiotic approach. As Roland Barthes (1984), any simple "design work (magazine cover)" continue to play in management of the sign. So that will generate a message (image) specific. Design cover, usually contains the elements of the sign in the form of objects, context of the environment, people or other beings who provide meaning to objects, and text (of writing) that reinforce the meaning.Keyword: cover, magazine Concept, semiotics

scholarly journals A Two-Stage Neural Network-Based Cold Start Item Recommender

2021 ◽  
Vol 11 (9) ◽  
pp. 4243
Author(s):  
Chieh-Yuan Tsai ◽  
Yi-Fan Chiu ◽  
Yu-Jen Chen
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Service Providers ◽  
Cold Start ◽  
Computational Time ◽  
Two Stage ◽  
The Neural Network ◽  
Textual Description ◽  
Recommendation Algorithms ◽  
Content Information

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.

scholarly journals An Integrated Credit-Based Incentive Protocol for Symbol-Level Network-Coded Cooperative Content Distribution among Vehicular Nodes

2018 ◽  
Vol 8 (11) ◽  
pp. 2035 ◽  
Author(s):  
Ing-Chau Chang ◽  
Chin-En Yen ◽  
Jacky Lo
Keyword(s):  
Ad Hoc ◽  
High Efficiency ◽  
Performance Metrics ◽  
Content Distribution ◽  
Long Latency ◽  
Distribution Process ◽  
Distribution Operations ◽  
The Difference ◽  
Different Content ◽  
Content Information

In traditional symbol-level network coding (SLNC)-based cooperative content distribution approaches, they ignore nodes in the vehicular ad hoc network (VANET) having various network-coded content pieces and distinct levels of interests and selfishness for different kinds of content data, which further prevents these vehicular nodes from forwarding their content information to other nodes. With these approaches, these nodes suffer from the low ratio and the long latency to receive all content information. In this paper, based on distinct levels of node interests and selfishness on different content information, we first categorize vehicular nodes into four classes, that is, the destination, intermediate, irrelevant and overhearing ones and then designate their associated credit-based incentive approaches. Second, we modify the flow of traditional SLNC-based cooperative content distribution operations and propose the content bitmap to realize the difference of network-coded content pieces among vehicular nodes. Further, we rigidly combine the proposed credit-based incentive approach with the modified SLNC-based cooperative content distribution operations in SocialCode to encourage all classes of vehicular nodes to rise their incentives for sharing content data in the cooperative content distribution process. Finally, we perform NS-2 simulations on a street map of downtown Taipei, Taiwan to exhibit the high efficiency of SocialCode over related credit-based incentive approaches by analyzing the following performance metrics, that is, average decoding percentage, file downloading delay and credits, with respect to different file sizes and total numbers of vehicular nodes. As the best knowledge we have, SocialCode is one of the first few researches that works on the integration between the credit-based incentive protocol and the SLNC-based cooperative content distribution.

Stream-based explainable recommendations via blockchain profiling

2021 ◽  
pp. 1-17
Author(s):  
Fátima Leal ◽  
Bruno Veloso ◽  
Benedita Malheiro ◽  
Juan Carlos Burguillo ◽  
Adriana E. Chis ◽  
...  
Keyword(s):  
Historical Data ◽  
Positive Impact ◽  
Empirical Evaluation ◽  
Recommendation Systems ◽  
Use Cases ◽  
Collaborative Filter ◽  
Model Based ◽  
Collaborative Recommendation ◽  
Smart Contract ◽  
User Trust

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

scholarly journals Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation

2018 ◽  
Author(s):  
Weizhi Ma ◽  
Min Zhang ◽  
Chenyang Wang ◽  
Cheng Luo ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Cold Start ◽  
Media Content ◽  
Learning Approaches ◽  
Target Domain ◽  
Underlying Assumption ◽  
Cross Media ◽  
Cross Platform ◽  
Rating Prediction ◽  
Cross Media Content ◽  
Content Information

Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users' preferences in the target domain. Thought to be unrelated, users' off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user's off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user's off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation.

scholarly journals From storytelling to Facebook. Content biases when retelling or sharing a story

2021 ◽  
Author(s):  
Alberto Acerbi
Keyword(s):  
Social Network ◽  
Cultural Evolution ◽  
Cultural Transmission ◽  
Specific Content ◽  
Oral Storytelling ◽  
Oral Transmission ◽  
Related Information ◽  
Transmission Chain ◽  
Set Up ◽  
Content Information

Cultural evolution researchers use transmission chain experiments to investigate which content is more likely to survive when transmitted from one individual to another. These experiments resemble oral storytelling, where individuals need to understand, memorise, and reproduce the content. However, prominent contemporary forms of cultural transmission—think an online sharing— only involve the willingness to transmit the content. Here I present two fully preregistered online experiments that explicitly investigated the differences between these two modalities of transmission. The first experiment (N=1080) examined whether negative content, information eliciting disgust, and threat-related information were better transmitted than their neutral counterpart in a traditional transmission chain set-up. The second experiment (N=1200), used the same material, but participants were asked whether they would share or not the content in two conditions: in a large anonymous social network, or with their friends, in their favourite social network. Negative content was both better transmitted in transmission chain experiments and shared more than its neutral counterpart. Threat-related information was successful in transmission chain experiments but not when sharing, and, finally, information eliciting disgust was not advantaged in either. Overall, the results present a composite picture, suggesting that the interactions between the specific content and the medium of transmission are important and, possibly, that content biases are stronger when memorisation and reproduction are involved in the transmission—like in oral transmission—than when they are not—like in online sharing.

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