{"title":"基于相关特征的推荐算法","authors":"Xuan Wang, Cui Zhu, Wenjun Zhu, Bingxin Xue","doi":"10.1109/AEMCSE55572.2022.00138","DOIUrl":null,"url":null,"abstract":"Ratings and reviews on e-commerce platforms often contain a lot of useful information. In recent years, researchers aim to mine more information from ratings and reviews to improve recommendation performance. However, researchers have not fully considered the effect of mining the relationship between comments on feature representation. On the basis of previous research, this paper proposes a deep recommendation model based on relative features. We mine the relative attention between comments, calculate the relative features of users and products based on the attention, and use the relative features to complete recommendation. Experiments show that the effect of the model proposed in this paper is better than the previous models.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recommendation Algorithm based on Relative Features\",\"authors\":\"Xuan Wang, Cui Zhu, Wenjun Zhu, Bingxin Xue\",\"doi\":\"10.1109/AEMCSE55572.2022.00138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ratings and reviews on e-commerce platforms often contain a lot of useful information. In recent years, researchers aim to mine more information from ratings and reviews to improve recommendation performance. However, researchers have not fully considered the effect of mining the relationship between comments on feature representation. On the basis of previous research, this paper proposes a deep recommendation model based on relative features. We mine the relative attention between comments, calculate the relative features of users and products based on the attention, and use the relative features to complete recommendation. Experiments show that the effect of the model proposed in this paper is better than the previous models.\",\"PeriodicalId\":309096,\"journal\":{\"name\":\"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEMCSE55572.2022.00138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEMCSE55572.2022.00138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recommendation Algorithm based on Relative Features
Ratings and reviews on e-commerce platforms often contain a lot of useful information. In recent years, researchers aim to mine more information from ratings and reviews to improve recommendation performance. However, researchers have not fully considered the effect of mining the relationship between comments on feature representation. On the basis of previous research, this paper proposes a deep recommendation model based on relative features. We mine the relative attention between comments, calculate the relative features of users and products based on the attention, and use the relative features to complete recommendation. Experiments show that the effect of the model proposed in this paper is better than the previous models.