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Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

Jianrui Chen, Zhihui Wang, Tingting Zhu, Fernando E. Rosas
2020 Complexity  
To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network.  ...  This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model.  ...  Conclusion In this paper, a novel vector dynamic evolutionary clustering recommendation algorithm DN-VCA based on double-layer network and attention mechanism is proposed.  ... 
doi:10.1155/2020/5206087 fatcat:x2dbqntyzvbafkckix7nemi43y

A Review for Recommender System Models and Deep Learning

F. Nagy, A. Haroun, Hatem Abdel-Kader, Arabi Keshk
2021 IJCI. International Journal of Computers and Information  
traditional technology, how deep learning-based recommendation systems works, deep learning for recommendations and open problems and the novel research trends on this field.  ...  In this paper we introduce an overview for the traditional recommendation systems models, the recommendation systems advantages and shortcoming, the recommendation systems challenges, common deep learning  ...  a graph clustering algorithm to split the road network of a city into smaller road clusters for evaluation the feature set of a road cluster reflects its properties.  ... 
doi:10.21608/ijci.2021.207864 fatcat:hdwzp3o4djcsdo6ubqfkdmu3o4

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Xinhua Wang, Yuchen Wang, Lei Guo, Liancheng Xu, Baozhong Gao, Fangai Liu, Wei Li
2021 Information  
Furthermore, to overcome the sparsity issue of students' borrowing behaviours, a clustering-based reinforcement learning algorithm is further developed.  ...  As the noisy interactions in students' borrowing sequences may harm the recommendation performance of a book recommender, we focus on refining recommendations via filtering out data noises.  ...  However, methods based on collaborative filtering have certain limitations: They tend to recommend popular items in collaborative filtering algorithms, so the algorithm is less exploratory.  ... 
doi:10.3390/info12050198 doaj:4ac92bb218cf469687cb00f7ad904f24 fatcat:hinnymrc2vc3jo47ipamujw6c4

New draft item [article]

Leyla Zhuhadar, Sebastian Ryszard Kruk, Jerry Daday
2016 Figshare  
In this article we review various Collaborative Semantic Filtering technologies for buildingSemantic MOOCs' management system, then, we present a prototype of a semantic middle-sized platformimplemented  ...  We envision a new generation of MOOCs that support interpretability with formalsemantics by using the SemanticWeb and the online social networks.  ...  instead of computing recommendations based on a social network artificially created by the collaborative filtering algorithm.  ... 
doi:10.6084/m9.figshare.3423689.v1 fatcat:asogsahrurebdhntxvhhs3qh2m

A Network Architecture for Distributed Event Based Systems in an Ubiquitous Sensing Scenario [article]

Cristina Muñoz, Pierre Leone
2014 arXiv   pre-print
As future work, we propose to study the properties of this new layer and to work on the design of Bloom filters to manage broker nodes.  ...  In this paper, we present a network architecture which merges the network and overlay layers of typical structured event-based systems.  ...  The cluster concept in the network of brokers can be improved in a dynamic scenario by enriching the topology management with predictions based on location [17] . 1) Probabilistic approaches: Probabilistic  ... 
arXiv:1408.3033v1 fatcat:z4kpttjqyzhwdmtanmlvjzv6ia


2019 Intelligent Data Analysis  
And finally Tang et al. in the last article of this issue explain collaborative filtering (CF), one of the most famous methods for building recommendation systems, which recommends relevant items to users  ...  The first article of this group by Guo et al. is about analysing drug-target interaction cluster analysis based on improving the density peaks clustering algorithm.  ... 
doi:10.3233/ida-190006 fatcat:fh66shjh6bh6rlnhl45fxqarqm

Neural Networks in Big Data and Web Search

Will Serrano
2018 Data  
This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems.  ...  The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications  ...  A recommender system based on a collaborative filtering application using the k-separability method [137] is built for every user on various stages: a collection of users is clustered into diverse categories  ... 
doi:10.3390/data4010007 fatcat:2irxpdvtfrclrbndkrubl5jvqq

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

Xinwei Ren, Wei Yang, Xianliang Jiang, Guang Jin, Yan Yu
2022 Sustainability  
To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism.  ...  We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide  ...  Collaborative filtering algorithm recommendations can be divided into user-based, and item-based depending on the object.  ... 
doi:10.3390/su14052907 doaj:8371c9e0f6d64ee9a45830a3629a8137 fatcat:epahechflbdapcqxjg4mottnuu

Recent Advances in Information Technology

Fei Yu, Chin-Chen Chang, Yiqin Lu, Jian Shu, Yan Gao, Guangxue Yue, Zuo Chen
2014 The Scientific World Journal  
Fu et al. in the paper entitled "A collaborative recommend algorithm based on bipartite community" propose a bipartite community partitioning algorithm according to the real data environment of collaborative  ...  The paper entitled "Dynamic cooperative clustering based power assignment: network capacity and lifetime efficient topology control in cooperative ad hoc networks" by X.-H.  ...  on Information Processing (ISIP 2013).  ... 
doi:10.1155/2014/746479 pmid:25110742 pmcid:PMC4119702 fatcat:f3yq2agpevcvdbxe44umitvxa4

A Systematic Mapping Review on MOOC Recommender Systems

Imranuddin, Ali Shariq Imran, Khan Muhammad, Nosheen Fayyaz, Muhammad Sajjad
2021 IEEE Access  
Collaborative filtering (CF): This approach relies on a user's behavior or user rating for items. It is based on similar 'users' to recommend content [187] .  ...  Similarly, course based recommender system proposed by Li and Li [88] utilized Correlated pattern-based recommendations that combines MOOC clusters (course based cluster and user based cluster) with  ... 
doi:10.1109/access.2021.3101039 fatcat:vnhraonfujgstdvcnpcwi6lxxe

Video Popularity Prediction: An Autoencoder Approach with Clustering

Yu-Tai Lin, Chia-Cheng Yen, Jia-Shung Wang
2020 IEEE Access  
For instance, several collaborative denoising autoencoder (CDAE) models have shown that their performance gains outperform that of the collaborative filtering based (CF-based) models.  ...  In order to improve the prediction accuracy, we also propose an autoencoder based recommendation algorithm with the help of K -means clustering that upgrades the performance of the original autoencoder  ...  COLLABORATIVE FILTERING BASED RECOMMENDER SYSTEMS Collaborative Filtering based (CF-based) methods can be divided into two categories: memory-based CF and model-based CF.  ... 
doi:10.1109/access.2020.3009253 fatcat:tbxlsch2zfeipcfjfkowcem5cq

Deep AutoEncoders in Recommender Systems: An Application about Internet of Things Service Recommendation

2021 Bilişim Teknolojileri Dergisi  
With this aim, this study proposes deep autoencoders methodology to recommend services and applications to users based on the devices they own.  ...  Deep autoencoders utilize neural networks to predict user service preference matrix. The data used in this study is constructed from a real-world Social IoT dataset.  ...  User-Based collaborative filtering is used to recommend products for sensitive scenarios [42] .  ... 
doi:10.17671/gazibtd.685500 fatcat:ka2ch5s35zbepo2j6z5dlzt3ke

A hybrid recommender system based-on link prediction for movie baskets analysis

Mohammadsadegh Vahidi Farashah, Akbar Etebarian, Reza Azmi, Reza Ebrahimzadeh Dastjerdi
2021 Journal of Big Data  
DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated  ...  Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user.  ...  The methods that have been studied by various researchers are collaborative and content-based filtering systems. Content-based systems classify users based on their demographic information.  ... 
doi:10.1186/s40537-021-00422-0 fatcat:tbusqxuewfh7fhwsq6kdr2gvhq

POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks

Liqiang Sun
2021 Journal of Information Processing Systems  
To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network.  ...  Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences.  ...  In [18] , a collaborative filtering recommendation algorithm was proposed based on information theory and bi-clustering.  ... 
doi:10.3745/jips.01.0068 dblp:journals/jips/Sun21 fatcat:2cm26u5xzndqtpchii43iewnq4

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective [article]

Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie
2022 arXiv   pre-print
Based on the classification, we systematically discuss these methods and highlight their correlations.  ...  In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective.  ...  filtering, which is designed for a recommendation task.  ... 
arXiv:2202.04822v2 fatcat:ydnbs75uancljonaqjmaz6c4qa
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