{"title":"Neural Network Approaches for Recommender Systems","authors":"","doi":"10.1134/s1064230723060126","DOIUrl":null,"url":null,"abstract":"<span> <h3>Abstract</h3> <p>Recommender systems are special algorithms that allow users to receive personalized recommendations on topics that interest them. Systems of this kind are widely used in various fields, for example, in e-commerce, provider services, social networks, etc. Together with classical approaches, neural networks have also become popular in recommender systems in recent years, which are gradually replacing traditional methods of collaborative filtering and content-based algorithms. However, neural networks require large computing resources, which often raises questions on whether an increase in quality will be justified and whether there be one at all. The neural network approach in recommender systems—the self-attentive sequential recommendation (SASRec) transformer model from Microsoft Recommenders—is studied and compared with the classic algorithm, the LightFM hybrid model. For training and validation, the data taken from a housing search application are used. It is proposed to use the hit rate as the main metric for comparison. The results of the experiments will help to understand which algorithms have higher accuracy in terms of predictions and recommendations. As an additional part, the clustering of user and object embeddings is considered.</p> </span>","PeriodicalId":50223,"journal":{"name":"Journal of Computer and Systems Sciences International","volume":"21 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer and Systems Sciences International","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1134/s1064230723060126","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Recommender systems are special algorithms that allow users to receive personalized recommendations on topics that interest them. Systems of this kind are widely used in various fields, for example, in e-commerce, provider services, social networks, etc. Together with classical approaches, neural networks have also become popular in recommender systems in recent years, which are gradually replacing traditional methods of collaborative filtering and content-based algorithms. However, neural networks require large computing resources, which often raises questions on whether an increase in quality will be justified and whether there be one at all. The neural network approach in recommender systems—the self-attentive sequential recommendation (SASRec) transformer model from Microsoft Recommenders—is studied and compared with the classic algorithm, the LightFM hybrid model. For training and validation, the data taken from a housing search application are used. It is proposed to use the hit rate as the main metric for comparison. The results of the experiments will help to understand which algorithms have higher accuracy in terms of predictions and recommendations. As an additional part, the clustering of user and object embeddings is considered.
期刊介绍：
Journal of Computer and System Sciences International is a journal published in collaboration with the Russian Academy of Sciences. It covers all areas of control theory and systems. The journal features papers on the theory and methods of control, as well as papers devoted to the study, design, modeling, development, and application of new control systems. The journal publishes papers that reflect contemporary research and development in the field of control. Particular attention is given to applications of computer methods and technologies to control theory and control engineering. The journal publishes proceedings of international scientific conferences in the form of collections of regular journal articles and reviews by top experts on topical problems of modern studies in control theory.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
