{"type":"ScholarlyArticle","title":"Mention Recommendation for Twitter with End-to-end Memory Network","authors":[{"name":"Haoran Huang","slug":"haoran-huang","given":"Haoran","family":"Huang","affiliation":"School of Computer Science Fudan University Shanghai , P.R.China"},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":"School of Computer Science Fudan University Shanghai , P.R.China"},{"name":"Xuanjing Huang","slug":"xuanjing-huang","given":"Xuanjing","family":"Huang","affiliation":"School of Computer Science Fudan University Shanghai , P.R.China"}],"abstract":"In this study, we investigated the problem of recommending usernames when people attempt to use the ``@'' sign to mention other people in twitter-like social media. With the extremely rapid development of social networking services, this problem has received considerable attention in recent years. Previous methods have studied the problem from different aspects. Because most of Twitter-like microblogging services limit the length of posts, statistical learning methods may be affected by the problems of word sparseness and synonyms. Although recent progress in neural word embedding methods have advanced the state-of-the-art in many natural language processing tasks, the benefits of word embedding have not been taken into consideration for this problem. In this work, we proposed a novel end-to-end memory network architecture to perform this task. We incorporated the interests of users with external memory. A hierarchical attention mechanism was also applied to better consider the interests of users. The experimental results on a dataset we collected from Twitter demonstrated that the proposed method could outperform state-of-the-art approaches.","doi":"10.24963/ijcai.2017/260","url":"https://ijcai.org/proceedings/2017/260","pdf":"https://ijcai.org/proceedings/2017/0260.pdf","markdown":"https://ijcai.org/proceedings/2017/260.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/260","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":260,"pages":{"first":1872,"last":1878},"keywords":["Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Chen and Manning, 2014] Danqi Chen and Christopher D Manning. 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