Shuo Wang, Hui Zhou, Mingxin Li, C. Li, Zhongying Zhao
{"title":"考虑用户购买动机和潜在交互的网络嵌入协同过滤模型","authors":"Shuo Wang, Hui Zhou, Mingxin Li, C. Li, Zhongying Zhao","doi":"10.1109/CCIS53392.2021.9754616","DOIUrl":null,"url":null,"abstract":"The user-item interactions in recommender systems can be modeled as a bipartite graph. With the bipartite graph, numerous researches have been devoted to learning the representation of the user’s fine-grained preferences to improve the performance of recommendation. However, the existing work cannot fully encode high-order collaborative interaction. To address the above problem, we propose a network embedding based collaborative filtering model equipped with user purchase motivation and potential interaction. Experimental results on three real datasets demonstrate that the proposed model outperforms the competitive baselines and significantly improves the recommending performance.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Network Embedding Based Collaborative Filtering Model Equipped With User Purchase Motivation and Potential Interactions\",\"authors\":\"Shuo Wang, Hui Zhou, Mingxin Li, C. Li, Zhongying Zhao\",\"doi\":\"10.1109/CCIS53392.2021.9754616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The user-item interactions in recommender systems can be modeled as a bipartite graph. With the bipartite graph, numerous researches have been devoted to learning the representation of the user’s fine-grained preferences to improve the performance of recommendation. However, the existing work cannot fully encode high-order collaborative interaction. To address the above problem, we propose a network embedding based collaborative filtering model equipped with user purchase motivation and potential interaction. Experimental results on three real datasets demonstrate that the proposed model outperforms the competitive baselines and significantly improves the recommending performance.\",\"PeriodicalId\":191226,\"journal\":{\"name\":\"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIS53392.2021.9754616\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIS53392.2021.9754616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Network Embedding Based Collaborative Filtering Model Equipped With User Purchase Motivation and Potential Interactions
The user-item interactions in recommender systems can be modeled as a bipartite graph. With the bipartite graph, numerous researches have been devoted to learning the representation of the user’s fine-grained preferences to improve the performance of recommendation. However, the existing work cannot fully encode high-order collaborative interaction. To address the above problem, we propose a network embedding based collaborative filtering model equipped with user purchase motivation and potential interaction. Experimental results on three real datasets demonstrate that the proposed model outperforms the competitive baselines and significantly improves the recommending performance.