Continuous Monitoring of Physiologic Variables with a Dedicated Minicomputer

comprehensive intensive care electrical system design

arthur (0) · Ibid

a computer-driven video display system for patient monitoring

gerth (0) · Ibid

Untitled work

arthur (1974) · Evaluation of Computer-Based Patient Monitoring Systems

a digital communication system for clinical application

blaine (1972) · Proc 25th ACEMB

Untitled work

blaine (1974) · High-Speed Digital Communications?A Design Study Applied to the Hospital Environment

Recommendation systems: Principles, methods and evaluation

Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015) · Egyptian Informatics Journal · doi:10.1016/j.eij.2015.06.005 · Cited by 1,328 · see citing works

Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models

Frichot, É., Schoville, S. D., Bouchard, G., & François, O. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst063 · Cited by 815 · see citing works

Clinical requirements of future patient monitoring in the intensive care unit: qualitative study

Poncette, A.-S., Spies, C., Mosch, L., Schieler, M., Weber-Carstens, S., Krampe, H., & Balzer, F. (2019) · JMIR Medical Informatics · doi:10.2196/13064 · Cited by 136 · see citing works

Towards latent context-aware recommendation systems

Unger, M., Bar, A., Shapira, B., et al. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.04.020 · Cited by 120 · see citing works

Representation learning via Dual-Autoencoder for recommendation

Zhuang, F., Zhang, Z., Qian, M., et al. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.03.009 · Cited by 118 · see citing works

A dynamic collaborative filtering system via a weighted clustering approach

Salah, A., Rogovschi, N., & Nadif, M. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.050 · Cited by 101 · see citing works

Recommender systems for product bundling

Beladev, M., Rokach, L., & Shapira, B. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.08.013 · Cited by 90 · see citing works

McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data

Mongia, A., Sengupta, D., & Majumdar, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00009 · Cited by 76 · see citing works

Prediction of circRNA-disease associations based on inductive matrix completion

Li, M., Liu, M., Bin, Y., & Xia, J. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0679-0 · Cited by 73 · see citing works

ISCMF: Integrated similarity-constrained matrix factorization for drug–drug interaction prediction

Rohani, N., Eslahchi, C., & Katanforoush, A. (2020) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-019-0215-3 · Cited by 60 · see citing works

A graph-based QoS prediction approach for web service recommendation

Chang, Z., Ding, D., & Xia, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02120-5 · Cited by 52 · see citing works

Detecting Staphylococcus aureus Virulence and Resistance Genes: a Comparison of Whole-Genome Sequencing and DNA Microarray Technology

Strauß, L., Ruffing, U., Abdulla, S., Alabi, A. S., Akulenko, R., Garrine, M., Germann, A., Grobusch, M. P., Helms, V., Herrmann, M., et al. (2016) · Journal of Clinical Microbiology · doi:10.1128/jcm.03022-15 · Cited by 52 · see citing works

A movie recommendation method based on users' positive and negative profiles

Chen, Y., Yeh, Y.-H., & Ma, M.-R. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102531 · Cited by 50 · see citing works

Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization

Shan, L., Lin, L., Sun, C., et al. (2016) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2016.01.004 · Cited by 48 · see citing works

Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering

Zhang, Z., Zhang, Y., & Ren, Y. (2020) · Information Retrieval · doi:10.1007/s10791-020-09378-w · Cited by 45 · see citing works

A hybrid collaborative filtering model for social influence prediction in event-based social networks

Li, X., Cheng, X., Su, S., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.12.024 · Cited by 41 · see citing works

Balancing accuracy and diversity in recommendations using matrix completion framework

Gogna, A., & Majumdar, A. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.03.023 · Cited by 38 · see citing works

Modeling Sequential Preferences with Dynamic User and Context Factors

Le, D.-T., Fang, Y., & Lauw, H. W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46227-1_10 · Cited by 37 · see citing works

Online robust principal component analysis via truncated nuclear norm regularization

Hong, B., Wei, L., Hu, Y., et al. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.052 · Cited by 34 · see citing works

Improved personalized recommendation based on a similarity network

Wang, X., Liu, Y., & Xiong, F. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.03.070 · Cited by 33 · see citing works

TAERT: Triple-Attentional Explainable Recommendation with Temporal Convolutional Network

Guo, S., Wang, Y., Yuan, H., Huang, Z., Chen, J., & Wang, X. (2021) · Information Sciences · doi:10.1016/j.ins.2021.03.034 · Cited by 34 · see citing works

Event-triggered nonfragile H ∞ filtering of Markov jump systems with imperfect transmissions

Shen, M., Park, J. H., & Fei, S. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.03.015 · Cited by 31 · see citing works

Design of normalized fractional SGD computing paradigm for recommender systems

Khan, Z. A., Zubair, S., Chaudhary, N. I., et al. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04562-6 · Cited by 29 · see citing works

Aggregating Functionality, Use History, and Popularity of APIs to Recommend Mashup Creation

Jain, A., Liu, X., & Yu, Q. (2015) · Lecture notes in computer science · doi:10.1007/978-3-662-48616-0_12 · Cited by 26 · see citing works

Design of reciprocal recommendation systems for online dating

Xia, P., Zhai, S., Liu, B., et al. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0340-2 · Cited by 26 · see citing works

Purchase prediction using Tmall‐specific features

Zhao, Y., Yao, L., & Zhang⋆, Y. (2016) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3720 · Cited by 24 · see citing works

Wasserstein autoencoders for collaborative filtering

Zhang, X., Zhong, J., & Liu, K. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05117-w · Cited by 23 · see citing works

Job recommendation algorithm for graduates based on personalized preference

Zhou, Q., Liao, F., Chen, C., et al. (2019) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-019-00022-1 · Cited by 21 · see citing works

Social network regularized Sparse Linear Model for Top-N recommendation

Feng, X., Sharma, A., Srivastava, J., et al. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.01.019 · Cited by 21 · see citing works

Interference-aware parallelization for deep learning workload in GPU cluster

Geng, X., Zhang, H., Zhao, Z., et al. (2020) · Cluster Computing · doi:10.1007/s10586-019-03037-6 · Cited by 21 · see citing works

Neighborhood-aware web service quality prediction using deep learning

Jin, Y., Wang, K., Zhang, Y., et al. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-019-1525-y · Cited by 20 · see citing works

Increasing prediction accuracy in collaborative filtering with initialized factor matrices

Nasiri, M., & Bidgoli, B. M. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1717-8 · Cited by 19 · see citing works

Community clustering based on trust modeling weighted by user interests in online social networks

Ullah, F., & Lee, S. (2017) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2017.05.041 · Cited by 19 · see citing works

A Collaborative Filtering Recommendation System for Rating Prediction

Davagdorj, K., Park, K. H., & Ryu, K. H. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-9714-1_29 · Cited by 20 · see citing works

Personalized recommendation: an enhanced hybrid collaborative filtering

Pirasteh, P., Bouguelia, M.-R., & Santosh, K. (2021) · Advances in Computational Intelligence · doi:10.1007/s43674-021-00001-z · Cited by 19 · see citing works

Confidence-weighted bias model for online collaborative filtering

Zhou, X., Shu, W., Lin, F., et al. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.07.005 · Cited by 18 · see citing works

Recurrent Convolution Basket Map for Diversity Next-Basket Recommendation

Leng, Y., Yu, L., Xiong, J., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59419-0_39 · Cited by 18 · see citing works

Utilizing transfer learning for in-domain collaborative filtering

Grolman, E., Bar, A., Shapira, B., et al. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.057 · Cited by 17 · see citing works

TimeFly algorithm: a novel behavior-inspired movie recommendation paradigm

Sinha, B. B., Dhanalakshmi, R., & Regmi, R. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00883-8 · Cited by 14 · see citing works

Recommendation System Based on Prediction of User Preference Changes

Inuzuka, K., Hayashi, T., & Takagi, T. (2016) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2016.0036 · Cited by 14 · see citing works

Geographical Proximity Boosted Recommendation Algorithms for Real Estate

Yu, Y., Wang, C., Li, Z., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02925-8_4 · Cited by 14 · see citing works

On Cross-Domain Transfer in Venue Recommendation

Manotumruksa, J., Rafailidis, D., Macdonald, C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-15712-8_29 · Cited by 14 · see citing works

Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-Based Recommendation

Zhu, J. H., Xu, Y., & Zhu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59419-0_40 · Cited by 14 · see citing works

Who should I invite: predicting event participants for a host user

Jiang, J., & Li, C. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1194-x · Cited by 13 · see citing works

A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information

Duong, T. N., Than, V. D., Vuong, T. A., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29029-0_30 · Cited by 13 · see citing works

PMD: An Optimal Transportation-Based User Distance for Recommender Systems

Meng, Y., Dai, X., Yan, X., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45442-5_34 · Cited by 12 · see citing works

Deep Learning-Based Recommender Systems

Alfarhood, M., & Cheng, J. J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-6759-9_1 · Cited by 12 · see citing works

Helpfulness-aware review based neural recommendation

Ge, S., Qi, T., Wu, C., et al. (2019) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-019-00023-0 · Cited by 11 · see citing works

Enhanced SVD for Collaborative Filtering

Guan, X., Li, C., & Guan, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31750-2_40 · Cited by 10 · see citing works

Surrogate Fitness via Factorization of Interaction Matrix

Liskowski, P., & Krawiec, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30668-1_5 · Cited by 10 · see citing works

Applying an Implicit Recommender System in the Preparation of Visits to Cultural Heritage Places

Cardoso, P. J. S., Guerreiro, P. M. M., Monteiro, J., & Rodrigues, J. M. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92052-8_33 · Cited by 10 · see citing works

Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks

Dai, F., Gu, X., Li, B., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22741-8_41 · Cited by 10 · see citing works

Mutual Self Attention Recommendation with Gated Fusion Between Ratings and Reviews

Peng, Q., Liu, H., Yu, Y., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59419-0_33 · Cited by 10 · see citing works

Review rating prediction using combined latent topics and associated sentiments: an empirical review

Mahadevan, A., & Arock, M. (2019) · Service Oriented Computing and Applications · doi:10.1007/s11761-019-00278-6 · Cited by 9 · see citing works

Modeling Periodic Pattern with Self-Attention Network for Sequential Recommendation

Ma, J., Zhao, P., Liu, Y., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59419-0_34 · Cited by 9 · see citing works

A novel recommendation scheme with multifactorial weighted matrix decomposition strategies via forgetting rule

Chen, J., Lu, Y., Shang, F., & Zhu, T. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104191 · Cited by 10 · see citing works

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

Tao, C., Nichols, T. E., Hua, X., Ching, C. R. K., Rolls, E. T., Thompson, P. M., & Feng, J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.027 · Cited by 9 · see citing works

A generalized model via random walks for information filtering

Ren, Z.-M., Kong, Y.-X., Shang, M., et al. (2016) · Physics Letters A · doi:10.1016/j.physleta.2016.06.009 · Cited by 8 · see citing works

An initialization method for the latent vectors in probabilistic matrix factorization for sparse datasets

Ar, Y. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00299-2 · Cited by 8 · see citing works

SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation

Vijaikumar, M., Shevade, S., & Murty, M. N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46150-8_26 · Cited by 8 · see citing works

CAMF: Context Aware Matrix Factorization for Social Recommendation

Gu, Y., Song, J., Liu, W., Zou, L., & Yao, Y. (2018) · Web Intelligence · doi:10.3233/web-180373 · Cited by 9 · see citing works

Taking e-Assessment Quizzes - A Case Study with an SVD Based Recommender System

Teodorescu, O. M., Popescu, P. Ș., & Mihăescu, M. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03493-1_86 · Cited by 8 · see citing works

Dynamic user modeling for expert recommendation in community question answering

He, T., Guo, C., Chu, Y., et al. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-200729 · Cited by 7 · see citing works

RACMF: robust attention convolutional matrix factorization for rating prediction

Zeng, B., Qi, S., Han, X., et al. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-019-00814-2 · Cited by 7 · see citing works

Towards Poisoning the Neural Collaborative Filtering-Based Recommender Systems

Zhang, Y., Lou, J., Chen, L., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58951-6_23 · Cited by 8 · see citing works

A multistep priority-based ranking for top-N recommendation using social and tag information

Banerjee, S., Banjare, P., Pal, B., & Jenamani, M. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02388-y · Cited by 7 · see citing works

Convolutional Neural Network-Based Collaborative Filtering for Recommendation Systems

Low, Y. H., Yap, W., & Tee, Y. K. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-7780-8_10 · Cited by 6 · see citing works

An Efficient Greedy Algorithm for Sequence Recommendation

Benouaret, I., Amer-Yahia, S., & Roy, S. B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27615-7_24 · Cited by 6 · see citing works

Link Prediction Model Based on the Topological Feature Learning for Complex Networks

Devi, S. J., & Singh, B. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04612-5 · Cited by 6 · see citing works

KASR: Knowledge-Aware Sequential Recommendation

Wang, Q., Xiong, Y., Zhu, Y., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60259-8_36 · Cited by 6 · see citing works

HTDA: Hierarchical time-based directional attention network for sequential user behavior modeling

Sheng, Z., Zhang, T., & Zhang, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.006 · Cited by 6 · see citing works

FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems

Xin, X., Wang, D., Ding, Y., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45817-5_8 · Cited by 5 · see citing works

Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter

Narayanan, S., Samuel, P., & Chacko, M. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00292-y · Cited by 5 · see citing works

Hierarchical Multi-view Attention for Neural Review-Based Recommendation

Liu, H., Wang, W., Chen, H., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60457-8_22 · Cited by 5 · see citing works

The effect of heterogeneous dynamics of online users on information filtering

Chen, B.-L., Zeng, A., & Chen, L. (2015) · Physics Letters A · doi:10.1016/j.physleta.2015.09.019 · Cited by 5 · see citing works

A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations

Tavakol, M., & Brefeld, U. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71246-8_17 · Cited by 5 · see citing works

MGCN4REC: Multi-graph Convolutional Network for Next Basket Recommendation with Instant Interest

Zhang, Y., Guo, B., Wang, Q., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64243-3_14 · Cited by 5 · see citing works

Causality-Aware Neighborhood Methods for Recommender Systems

Sato, M., Singh, J., Takemori, S., & Zhang, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_40 · Cited by 5 · see citing works

A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems

Yu, P., Lin, L., & Yao, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39937-9_18 · Cited by 5 · see citing works

An In-store Recommender System Leveraging the Microsoft HoloLens

Mora, D., Jain, S., Nalbach, O., & Werth, D. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-50729-9_14 · Cited by 5 · see citing works

Factorization threshold models for scale-free networks generation

Artikov, A., Dorodnykh, A., Kashinskaya, Y., et al. (2016) · Computational Social Networks · doi:10.1186/s40649-016-0029-8 · Cited by 4 · see citing works

Recommendation System Based on Deep Learning

Gao, T., Jiang, L., & Wang, X. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-33506-9_48 · Cited by 4 · see citing works

Personalized Recommender Systems with Multi-source Data

Wang, Y., Wu, T., Ma, F., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-52249-0_15 · Cited by 4 · see citing works

A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts

Xu, H., Wu, P., Wei, J., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26075-0_17 · Cited by 4 · see citing works

User Preference Multi-criteria Recommendations Using Neural Collaborative Filtering Methods

Goud, K. N., Ramanjaneyulu, Y. V., Babu, K. S., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-8061-1_5 · Cited by 4 · see citing works

Social-group-based ranking algorithms for cold-start video recommendation

Yang, C.-F., Zhou, Y., Chen, L., Zhang, X., & Chiu, D. (2016) · International Journal of Data Science and Analysis · doi:10.1007/s41060-016-0015-0 · Cited by 4 · see citing works

Modeling User Mobility via User Psychological and Geographical Behaviors Towards Point of-Interest Recommendation

Chen, Y., Li, X., Li, L., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-32025-0_23 · Cited by 3 · see citing works

Improving Recommendation Accuracy for Travelers by Exploiting POI Correlations

Zhang, P., Zhao, D., & Wang, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45817-5_11 · Cited by 3 · see citing works

LSPM: Joint Deep Modeling of Long-Term Preference and Short-Term Preference for Recommendation

Chen, J., Jiang, L., Sun, H., et al. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36808-1_26 · Cited by 3 · see citing works

Leveraging Kernel Incorporated Matrix Factorization for Smartphone Application Recommendation

Liu, C., Cao, J., & He, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55753-3_29 · Cited by 3 · see citing works

Crowdsourced Time-Sync Video Recommendation via Semantic-Aware Neural Collaborative Filtering

Wu, Z., Zhou, Y., Wu, D., Zhou, Y., & Qin, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-19274-7_13 · Cited by 3 · see citing works

Deep Structured Semantic Model for Recommendations in E-commerce

Larionova, А. A., Kazakova, P., & Nikitinsky, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29859-3_8 · Cited by 3 · see citing works

Leveraging Arguments in User Reviews for Generating and Explaining Recommendations

Donkers, T., & Ziegler, J. (2020) · Datenbank-Spektrum · doi:10.1007/s13222-020-00350-y · Cited by 3 · see citing works

Knowledge Graph Attention Network Enhanced Sequential Recommendation

Zhu, X., Zhao, P., Xu, J., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60259-8_15 · Cited by 3 · see citing works

Perspectives in Clinical Computing

O'Kane, K. C., & Haluska, E. A. (1977) · Advances in computers · doi:10.1016/s0065-2458(08)60116-3 · Cited by 3 · see citing works

Frontiers of AI in Education

Yu, S., & Lu, Y. (2021) · Bridging human and machine: future education with intelligence · doi:10.1007/978-981-16-2770-5_8 · Cited by 3 · see citing works

Local Weighted Matrix Factorization for Implicit Feedback Datasets

Wang, K., Duan, X., Ma, J., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-32025-0_24 · Cited by 2 · see citing works

Vector Representation Based Model Considering Randomness of User Mobility for Predicting Potential Users

Peng, S., Xie, X., Mine, T., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03098-8_5 · Cited by 2 · see citing works

NRSA: Neural Recommendation with Summary-Aware Attention

Peng, Q., Wang, P., Wang, W., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29551-6_12 · Cited by 2 · see citing works

Microdiagnostics for minicomputers

Andrews, M. (1973) · Annual Workshop on Microprogramming (MICRO) · doi:10.1145/800203.806246

Minicomputer as a circuit-design tool

Fazarinc, Z. (1972) · SIMULATION · doi:10.1177/003754977201800204 · Cited by 11 · see citing works

A Minicomputer Numbercruncher

Lindgård, A., Sørensen, P. G., & Øxenbøll, J. (1977) · ACS symposium series · doi:10.1021/bk-1977-0057.ch010

Overview of a minicomputer network

Vahle, M. O., & Tolendino, L. F. (1980) · doi:10.2172/5047754

Minicomputer-based health sciences instruction (summary report)

Brigham, C. R. (1974) · ACM SIGCUE Outlook · doi:10.1145/1316855.1316857