A deep recommendation model of cross-grained sentiments of user reviews and ratings

Ait Hammou, 2020, Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics, Information Processing and Management, 57, 10.1016/j.ipm.2019.102122

Baccianella, 2010, SENTIWORDNET 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, 2200

Bao, 2014, TopicMF: Simultaneously exploiting ratings and reviews for recommendation, 1, 2

Belém, 2020, Fixing the curse of the bad product descriptions” – search-boosted tag recommendation for E-commerce products, Information Processing and Management, 57, 10.1016/j.ipm.2020.102289

Catherine, 2017, TransNets: Learning to transform for recommendation

Chambua, 2018, Tensor factorization method based on review text semantic similarity for rating prediction, Expert Systems With Applications, 114, 629, 10.1016/j.eswa.2018.07.059

Chen, 2018, Neural attentional rating regression with review-level explanations, 1583

Chen, 2019, Effective selection of a compact and high-quality review set with information preservation, ACM Transactions on Management Information Systems, 10, 1, 10.1145/3369395

Chen, 2020, Eating healthier: Exploring nutrition information for healthier recipe recommendation, Information Processing and Management, 57, 10.1016/j.ipm.2019.05.012

Cheng, 2019, MMalfM: Explainable recommendation by leveraging reviews and images, ACM Transactions on Information Systems, 37, 10.1145/3291060

Cobos, 2013, A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes, Information Processing and Management, 49, 607, 10.1016/j.ipm.2012.12.002

Feng, 2019, Social and comment text CNN model based automobile recommendation, Zidonghua Xuebao/Acta Automatica Sinica, 45, 518

Forman, 2008, Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets, 19, 291

Greco, 2020, Emotional text mining: Customer profiling in brand management, International Journal of Information Management, 51, 10.1016/j.ijinfomgt.2019.04.007

Guzman, 2014, How do users like this feature? A fine grained sentiment analysis of App reviews, 153

Hong, 2020, A parallel deep neural network using reviews and item metadata for cross-domain recommendation, IEEE Access, 8, 41774, 10.1109/ACCESS.2020.2977123

Huang, 2019, TRec: an efficient recommendation system for hunting passengers with deep neural networks, Neural Computing and Applications, 31, 209, 10.1007/s00521-018-3728-2

Jagtap, 2013, Analysis of different approaches to sentence-level sentiment classification, International Journal of Scientific Engineering and Technology, 2, 164

Ji, 2016, Jointly modeling content, social network and ratings for explainable and cold-start recommendation, Neurocomputing, 218, 1, 10.1016/j.neucom.2016.03.070

Kim, 2016, Compression of deep convolutional neural networks for fast and low power mobile applications

Kleenankandy, 2020, An enhanced tree-LSTM architecture for sentence semantic modeling using typed dependencies, Information Processing and Management, 57, 10.1016/j.ipm.2020.102362

Koren, 2008, Factorization meets the neighborhood: A multifaceted collaborative filtering model, 426

Koren, 2009, Matrix factorization techniques for recommender systems, Computer, 42, 30, 10.1109/MC.2009.263

Kumar, 2020, Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data, Information Processing and Management, 57, 10.1016/j.ipm.2019.102141

Lima, 2008, From mating pool distributions to model overfitting, 431

Linden, 2003, Amazon.com recommendations: item-to-item collaborative filtering, IEEE Internet Computing, 7, 76, 10.1109/MIC.2003.1167344

Liu, 2021, A hybrid neural network approach to combine textual information and rating information for item recommendation, Knowledge and Information Systems, 63, 621, 10.1007/s10115-020-01528-2

Liu, 2010, An improved collaborative filtering recommendation algorithm, 194

Liu, 2020, Attention-based adaptive memory network for recommendation with review and rating, IEEE Access, 8, 113953, 10.1109/ACCESS.2020.2997115

Liu, 2014, Content-boosted restricted boltzmann machine for recommendation, 773

Lizarralde, 2020, Discovering web services in social web service repositories using deep variational autoencoders, Information Processing and Management, 57, 10.1016/j.ipm.2020.102231

Luo, 2019, Personalized recommendation by matrix co-factorization with tags and time information, Expert Systems with Applications, 119, 311, 10.1016/j.eswa.2018.11.003

Luo, 2014, An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Transactions on Industrial Informatics, 10, 1273, 10.1109/TII.2014.2308433

Peng, 2017, Collaborative filtering recommendation based on sentiment analysis and LDA topic model, Journal of Chinese Information Processing

Mikolov, 2013

Mitra, 2021, Helpfulness of online consumer reviews: A multi-perspective approach, Information Processing and Management, 58, 10.1016/j.ipm.2021.102538

Ni, 2020, Justifying recommendations using distantly-labeled reviews and fine-grained aspects, 188

Pan, 2017, Ratings distribution recommendation model-based collaborative filtering recommendation algorithm, DEStech Transactions on Computer Science and Engineering, 10.12783/dtcse/smce2017/12456

Rao, 2014, Building emotional dictionary for sentiment analysis of online news, World Wide Web, 17, 723, 10.1007/s11280-013-0221-9

Rice, L., Wong, E., & Kolter, J. Z. (2020). Overfitting in adversarially robust deep learning. 37th International Conference on Machine Learning, ICML 2020, PartF16814, 80498074.

Salakhutdinov, 2009, Probabilistic matrix factorization, 1

Seo, 2017, Interpretable convolutional neural networks with dual local and global attention for review rating prediction, 297

Shi, 2010, An attribute-based sentiment analysis system, Information Technology Journal, 9, 1607, 10.3923/itj.2010.1607.1614

Singh, 2021, Accelerated optimization of curvilinearly stiffened panels using deep learning, Thin-Walled Structures, 161, 10.1016/j.tws.2020.107418

Srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15

Su, 2009, A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009, 1, 10.1155/2009/421425

Sun, 2019, Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level, Engineering Applications of Artificial Intelligence, 81, 68, 10.1016/j.engappai.2019.02.004

Symeonidis, 2021, Session-based news recommendations using SimRank on multi-modal graphs, Expert Systems With Applications, 180, 10.1016/j.eswa.2021.115028

Tang, 2020, Text semantic understanding based on knowledge enhancement and multi-granular feature extraction, 337

Wang, 2014, The collaborative filtering recommendation based on sentiment analysis of online reviews, Xitong Gongcheng Lilun Yu Shijian/System Engineering Theory and Practice, 34, 3238

Wang, 2020, A deep neural network of multi-form alliances for personalized recommendations, Information Sciences, 531, 68, 10.1016/j.ins.2020.03.062

Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. 69, 29–39. 10.1016/j.eswa.2016.09.040.

Wu, 2020, A data-characteristic-aware latent factor model for web services QoS prediction, IEEE Transactions on Knowledge and Data Engineering, 10.1109/TKDE.2020.3014302

Yang, 2021, Leveraging semantic features for recommendation: Sentence-level emotion analysis, Information Processing and Management, 58, 10.1016/j.ipm.2021.102543

Yang, 2020, EEG-based emotion classification based on bidirectional long short-term memory network, Procedia Computer Science, 174, 491, 10.1016/j.procs.2020.06.117

Yang, 2020, An Approach to Alleviate the sparsity problem of hybrid collaborative filtering based recommendations: The product-attribute perspective from user reviews, Mobile Networks and Applications, 25, 376, 10.1007/s11036-019-01246-2

Younes, 2018, A performance evaluation of a fault-tolerant path recommendation protocol for smart transportation system, Wireless Networks, 24, 345, 10.1007/s11276-016-1335-7

Zeng, 2016, A restaurant recommender system based on user preference and location in mobile environment, 55

Zeng, 2016, Collaborative filtering recommendation algorithm optimization based on user attributes, 1, 580

Zhang, 2021, A deep bi-directional prediction model for live streaming recommendation, Information Processing and Management, 58, 10.1016/j.ipm.2020.102453

Zheng, 2021, Heterogeneous type-specific entity representation learning for recommendations in e-commerce network, Information Processing and Management, 58, 10.1016/j.ipm.2021.102629

Zheng, L., Noroozi, V., & Yu, P. S. (2017). Joint deep modeling of users and items using reviews for recommendation. WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 425433. https://doi.org/10.1145/3018661.3018665.

Zhu, 2021, Enhancing traceability of infectious diseases: A blockchain-based approach, Information Processing & Management, 58, 10.1016/j.ipm.2021.102570

Zhu, 2020, Understanding promotion framing effect on purchase intention of elderly mobile app consumers, Electronic Commerce Research and Applications, 44, 10.1016/j.elerap.2020.101010

Zirn, 2011, Fine-Grained Sentiment Analysis with Structural Features, Proceedings of the 5th International Joint Conference on Natural Language Processing, 336