{"title":"MVL:新闻推荐的多视角学习","authors":"Santosh T.Y.S.S, Avirup Saha, Niloy Ganguly","doi":"10.1145/3397271.3401294","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view. In the content view, we use a news encoder to learn news representations from different information like titles, bodies and categories. We obtain representation of user from his/her browsed news conditioned on the candidate news article to be recommended. In the graph-view, we propose to use a graph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propose to incorporate attention mechanism into the graph neural network to model the importance of these interactions for more informative representation learning of user and news. Experiments on a real world dataset validate the effectiveness of MVL.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"MVL: Multi-View Learning for News Recommendation\",\"authors\":\"Santosh T.Y.S.S, Avirup Saha, Niloy Ganguly\",\"doi\":\"10.1145/3397271.3401294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view. In the content view, we use a news encoder to learn news representations from different information like titles, bodies and categories. We obtain representation of user from his/her browsed news conditioned on the candidate news article to be recommended. In the graph-view, we propose to use a graph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propose to incorporate attention mechanism into the graph neural network to model the importance of these interactions for more informative representation learning of user and news. Experiments on a real world dataset validate the effectiveness of MVL.\",\"PeriodicalId\":252050,\"journal\":{\"name\":\"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3397271.3401294\",\"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 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397271.3401294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view. In the content view, we use a news encoder to learn news representations from different information like titles, bodies and categories. We obtain representation of user from his/her browsed news conditioned on the candidate news article to be recommended. In the graph-view, we propose to use a graph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propose to incorporate attention mechanism into the graph neural network to model the importance of these interactions for more informative representation learning of user and news. Experiments on a real world dataset validate the effectiveness of MVL.
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