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15th Conference on Bioinformatics Computational Biology and Health Informatics

作者： Gao, Qi Li, Jialin Han, Guosheng Zeng, Li Xiangtan Univ Sch Math & Computat Sci Xiangtan Peoples R China

ISBN: (纸本)9798400713026

A growing number of long non-coding RNAs have been found to be associated with various diseases, highlighting the significance of predicting lncRNA-disease associations (LDAs) for disease diagnosis and treatment. In light of the time-consuming and laborious nature of traditional biological experiments, numerous algorithms have been developed to predict unknown LDAs. To predict potential LDAs, we propose a model called GMF-MGCN-LDA, that leverages the advantages of graph convolutional networks and generalized matrix factorization. Firstly, in order to extract high-order nonlinear feature of lncRNA and disease, the hypergraph regularization term is introduced into the generalized matrix factorization framework to learn latent feature of lncRNA and disease. Next, in order to enrich the feature of lncRNA and disease, the graph convolutional networks is employed to learn nonlinear features from diverse similarity graph views, followed by the attention mechanism that adaptively assigns weights to these features. Finally, the convolutional neural networks is utilized to integrate features from each view. The features learned by generalized matrix factorization and convolutional neural networks are concatenated to obtain the final lncRNA and disease feature. Based on known LDAs, the two final features are further concatenated and merged into one feature as input to the CatBoost classifier to output the predicted association probability of lncRNA-disease pairs. The experimental results not only demonstrate the effectiveness of the components in GMF-MGCN-LDA but also establish its performance is better than or comparable to other models. Furthermore, the case study underscores the model's reliable prediction capability.

关键词： lncRNA-disease associations generalized matrix factorization graph neural networks attention mechanism

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Deep Plot-Aware generalized matrix factorization for Collaborative Filtering

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NEURAL PROCESSING LETTERS 2020年第3期52卷 1983-1995页

作者： Sun, Xiaoxin Zhang, Haobo Wang, Meiqi Yu, Mengying Yin, Minghao Zhang, Bangzuo Northeast Normal Univ Changchun Peoples R China

Fusing auxiliary information into ratings has shown promising performance for many recommendation tasks, such as age, sex, vocation of users or actors, director, genre, reviews of movies. However, all above auxiliary information is still sparse and not informative. For movie recommendations, besides the above information, there exists richer information in plot texts, exerting huge impacts on improving the recommendation accuracy. In this paper, we explore effective fusion of movie ratings and plot texts, we propose a deep plot-aware generalized matrix factorization for collaborative filtering model, which effectively combines both ratings and plot texts to implement a generalized collaborative filtering. To verify our proposal, we conduct extensive experiments on two popular datasets, and the results perform better than other state-of-the-art approaches in common recommendation tasks.

关键词： Plot representation generalized matrix factorization Information fusion Convolutional neural networks

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Learning resource recommendation based on generalized matrix factorization and Long Short-Term Memory model 11

Learning resource recommendation based on Generalized Matrix...

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11th IEEE Int Conf on Cloud Comp Technol and Sci (CloudCom) / 19th IEEE Int Conf Comp and Informat Technol (CIT) / 2019 Int Workshop on Resource Brokering with Blockchain (RBchain) / Asia-Pacific Serv Comp Conf (APSCC)

作者： Guo, Tianhang Wen, Yiping Wang, Feiran Hou, Junjie Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Key Lab Knowledge Proc & Networked Mfg Xiangtan Peoples R China

ISBN: (纸本)9781728150116

Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally can't deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named G-LSTM. Our model integrates the generalized matrix factorization (GMF) with Long Short-Term Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.

关键词： learning resource recommendation generalized matrix factorization long short-term memory cloud-based teaming application

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Multivariate quasi-tight framelets with high balancing orders derived from any compactly supported refinable vector functions

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Science China Mathematics 2022年第1期65卷 81-110页

作者： Bin Han Ran Lu Department of Mathematical and Statistical Sciences University of AlbertaEdmontonAB T 6G 2G1Canada

Generalizing wavelets by adding desired redundancy and flexibility,framelets(i.e.,wavelet frames)are of interest and importance in many applications such as image processing and numerical *** key properties of framelets are high vanishing moments for sparse multiscale representation,fast framelet transforms for numerical efficiency,and redundancy for ***,it is a challenging problem to study and construct multivariate nonseparable framelets,mainly due to their intrinsic connections to factorization and syzygy modules of multivariate polynomial ***,all the known multivariate tight framelets derived from spline refinable scalar functions have only one vanishing moment,and framelets derived from refinable vector functions are barely studied yet in the *** this paper,we circumvent the above difficulties through the approach of quasi-tight framelets,which behave almost identically to tight *** the popular oblique extension principle(OEP),from an arbitrary compactly supported M-refinable vector functionφwith multiplicity greater than one,we prove that we can always derive fromφa compactly supported multivariate quasi-tight framelet such that:(i)all the framelet generators have the highest possible order of vanishing moments;(ii)its associated fast framelet transform has the highest balancing order and is *** a refinable scalar functionφ(i.e.,its multiplicity is one),the above item(ii)often cannot be achieved intrinsically but we show that we can always construct a compactly supported OEP-based multivariate quasi-tight framelet derived fromφsatisfying item(i).We point out that constructing OEP-based quasi-tight framelets is closely related to the generalized spectral factorization of Hermitian trigonometric polynomial *** proof is critically built on a newly developed result on the normal form of a matrix-valued filter,which is of interest and importance in itself for greatly facilitating

关键词： quasi-tight multiframelet oblique extension principle refinable vector function vanishing moment balancing order compact framelet transform normal form of filters generalized matrix factorization

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Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

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Computers, Materials & Continua 2021年第7期68卷 487-503页

作者： Thavavel Vaiyapuri College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever ***,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate ***,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of *** of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality *** by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF *** proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in ***,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item ***,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP *** conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation *** experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation *** analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rat

关键词： Neural collaborative filtering cold-start problem data sparsity multilayer perception generalized matrix factorization autoencoder deep learning ensemble learning top-K recommendations

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Attribute-aware deep attentive recommendation

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JOURNAL OF SUPERCOMPUTING 2021年第6期77卷 5510-5527页

作者： Sun, Xiaoxin Zhang, Lisa Wang, Yuling Yu, Mengying Yin, Minghao Zhang, Bangzuo Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China

Since the rich semantics of attribute information has become a great supplement to the ratings data in designing recommender systems, fusing attributes information into ratings has shown promising performance for many recommendation tasks. However, the use of attribute information is not easy, because different attributes are often: (1) multi-source, that is, attributes may come from many different fields, (2) unstructured, (3) unbalanced, (4) heterogeneous. In this paper, we explore effective fusion of user-item ratings and item attributes to improve recommendations, we propose an attribute-aware deep attentive recommendation model, which embeds attribute information into the latent semantic space of items through the attention mechanism, forming more accurate item representations. Extensive experiments show that our method is superior to the existing methods on both rating prediction and Top-N Recommendation tasks.

关键词： Attribute learning generalized matrix factorization Information fusion Attention mechanism

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On deep neural network for trust aware cross domain recommendations in E-commerce

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114757-114757页

作者： Ahmed, Adeel Saleem, Khalid Khalid, Osman Rashid, Umer Quaid I Azam Univ Dept Comp Sci Islamabad 45320 Pakistan COMSATS Univ Dept Comp Sci Abbottabad Campus Islamabad Pakistan

Over the years, cross-domain and trust-based recommendation systems are proven to be very helpful in solving issues pertaining to data sparsity and cold start. Many e-commerce sites used recommender systems as business tools for increasing their sale productivity and help their customers in finding suitable products. However, due to sparse rating and lack of historical information, such systems cannot generate effective recommendations. matrix factorization and deep learning techniques have been the focus of research community for the last few years to solve the problem of data sparsity and cold start. In this paper, we have proposed a model called Trust Aware Cross-Domain Deep Neural matrix factorization (TCrossDNMF) that predicts rating of an item for an active user and solves user cold start problem in the cross-domain scenario of 'User Overlap' in e-commerce system. TCrossDNMF model is divided into four main steps: i) Features learning that learns the users' features using a latent factor model and then finds the similarity between users of source and target domains. As the users are shared between two domains, the proposed model learns the common information and transfers the knowledge from a source to target domain. ii) Ranking that finds set of similar users (neighbors), and then filters out the dissimilar users based on similarity threshold theta, and then generates a bipartite trust graph from these reduced set of users and executes Ant Colony Optimization, to find trustworthy neighbors for an active user. iii) Weighting computes the trust degree between an active user and his or her top-k neighbors. iv) Prediction that trains the TCrossDNMF model using multilayer perceptron (MLP) and generalized matrix factorization (GMF) by repre-senting user-item interactions in higher dimensions and ensembles the GMF and MLP with trust information for rating prediction. We evaluated proposed model on a real dataset collected from a popular e-commerce retail service 'Ali

关键词： Ant colony optimization Bipartite graph Collaborative filtering Cross domain Ecommerce systems generalized matrix factorization Implicit trust Multilayer perceptron

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Learning-to-Ask: Knowledge Acquisition via 20 Questions 18

Learning-to-Ask: Knowledge Acquisition via 20 Questions

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24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Chen, Yihong Chen, Bei Duan, Xuguang Lou, Jian-Guang Wang, Yue Zhu, Wenwu Cao, Yong Tsinghua Univ Dept Electr Engin Beijing Peoples R China Microsoft Res Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Alibaba AI Labs Beijing Peoples R China

ISBN: (纸本)9781450355520

Almost all the knowledge empowered applications rely upon accurate knowledge, which has to be either collected manually with high cost, or extracted automatically with unignorable errors. In this paper, we study 20 Questions, an online interactive game where each question-response pair corresponds to a fact of the target entity, to acquire highly accurate knowledge effectively with nearly zero labor cost. Knowledge acquisition via 20 Questions predominantly presents two challenges to the intelligent agent playing games with human players. The first one is to seek enough information and identify the target entity with as few questions as possible, while the second one is to leverage the remaining questioning opportunities to acquire valuable knowledge effectively, both of which count on good questioning strategies. To address these challenges, we propose the Learning-to-Ask (LA) framework, within which the agent learns smart questioning strategies for information seeking and knowledge acquisition by means of deep reinforcement learning and generalized matrix factorization respectively. In addition, a Bayesian approach to represent knowledge is adopted to ensure robustness to noisy user responses. Simulating experiments on real data show that LA is able to equip the agent with effective questioning strategies, which result in high winning rates and rapid knowledge acquisition. Moreover, the questioning strategies for information seeking and knowledge acquisition boost the performance of each other, allowing the agent to start with a relatively small knowledge set and quickly improve its knowledge base in the absence of constant human supervision.

关键词： Knowledge Acquisition Information Seeking 20 Questions Reinforcement Learning generalized matrix factorization

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