{"title":"基于深度学习的推荐系统:模型、数据集、评估指标和未来趋势概述","authors":"Kyle Ong, S. Haw, K. Ng","doi":"10.1145/3372422.3372444","DOIUrl":null,"url":null,"abstract":"The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends\",\"authors\":\"Kyle Ong, S. Haw, K. Ng\",\"doi\":\"10.1145/3372422.3372444\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.\",\"PeriodicalId\":118684,\"journal\":{\"name\":\"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3372422.3372444\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3372422.3372444","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends
The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.