scholarly journals A Hybrid Recommendation System for Marine Science Observation Data Based on Content and Literature Filtering

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6414
Author(s):  
Xiaoyang Song ◽  
Yonggang Guo ◽  
Yongguo Chang ◽  
Fei Zhang ◽  
Junfeng Tan ◽  
...  
Keyword(s):  
Recommendation System ◽  
Observation Data ◽  
Marine Science ◽  
Data Correlation ◽  
Science Data ◽  
Filtering Algorithms ◽  
Valuable Data ◽  
Data Similarity ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

With the development of ocean exploration technology and the rapid growth in the amount of marine science observation data, people are faced with a great challenge to identify valuable data from the massive ocean observation data. A recommendation system is an effective method to improve retrieval capabilities to help users obtain valuable data. The two most popular recommendation algorithms are collaborative filtering algorithms and content-based filtering algorithms, which may not work well for marine science observation data given the complexity of data attributes and lack of user information. In this study, an approach was proposed based on data similarity and data correlation. Data similarity was calculated by analyzing the subject, source, spatial, and temporal attributes to obtain the recommendation list. Then, data correlation was calculated based on the literature on marine science data and ranking of the recommendation list to obtain the re-rank recommendation list. The approach was tested by simulated datasets collected from multiple marine data sharing websites, and the result suggested that the proposed method exhibits better effectiveness.

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

2021 ◽  
Vol 25 (4) ◽  
pp. 1013-1029
Author(s):  
Zeeshan Zeeshan ◽  
Qurat ul Ain ◽  
Uzair Aslam Bhatti ◽  
Waqar Hussain Memon ◽  
Sajid Ali ◽  
...  
Keyword(s):  
Recommendation System ◽  
Sources Of Information ◽  
Multiple Features ◽  
Recommendation Algorithms ◽  
Label Correlations ◽  
Feature Based ◽  
Multicriteria Model ◽  
Implicit And Explicit ◽  
Hybrid Recommendation ◽  
Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

scholarly journals Autonomous recommender system architecture for virtual learning environments

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Julián Monsalve-Pulido ◽  
Jose Aguilar ◽  
Edwin Montoya ◽  
Camilo Salazar
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Recommendation System ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Specific Recommendation ◽  
Digital Resources ◽  
Recommendation Algorithms ◽  
Available Information ◽  
Hybrid Recommendation

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

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.

On losing and recovering fisheries and marine science data

Marine Policy ◽  
2005 ◽  
Vol 29 (1) ◽  
pp. 69-73 ◽  
Author(s):  
Dirk Zeller ◽  
Rainer Froese ◽  
Daniel Pauly
Keyword(s):  
Marine Science ◽  
Science Data

scholarly journals Hybrid Recommendation System with Review Helpfulness Features

IARJSET ◽  
2017 ◽  
Vol 4 (4) ◽  
pp. 148-151
Author(s):  
Miss. Jadhav Monika ◽  
Mrs. Kakade Shital P
Keyword(s):  
Recommendation System ◽  
Review Helpfulness ◽  
Hybrid Recommendation

scholarly journals Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product

2020 ◽  
Vol 12 (1) ◽  
pp. 611-628 ◽  
Author(s):  
Michel M. Verstraete ◽  
Linda A. Hunt ◽  
Hugo De Lemos ◽  
Larry Di Girolamo
Keyword(s):  
Missing Values ◽  
Near Infrared ◽  
Data Repository ◽  
Observation Data ◽  
Science Data ◽  
Data Services ◽  
Data Product ◽  
Spectral Bands ◽  
Earth Observation Data ◽  
Cloud Mask

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software.

scholarly journals Using the Generative Adversarial Network to Generate Recommendations

2020 ◽  
Author(s):  
A.V. Prosvetov
Keyword(s):  
Real World ◽  
Recommendation System ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Theoretical Comparison ◽  
New Methods ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Recommendation Algorithms ◽  
Cold Start Problem

Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.

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