Learning-to-Rank for Real-Time High-Precision Hashtag Recommendation for Streaming News

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Title: Learning-to-Rank for Real-Time High-Precision Hashtag Recommendation for Streaming News
Authors: Ifrim, Georgiana
Shi, Bichen
Hurley, Neil J.
Permanent link: http://hdl.handle.net/10197/7359
Date: 15-Apr-2016
Abstract: We address the problem of real-time recommendation ofstreaming Twitter hashtags to an incoming stream of newsarticles. The technical challenge can be framed as largescale topic classication where the set of topics (i.e., hashtags)is huge and highly dynamic. Our main applicationscome from digital journalism, e.g., for promoting originalcontent to Twitter communities and for social indexing ofnews to enable better retrieval, story tracking and summarisation.In contrast to state-of-the-art methods that focus onmodelling each individual hashtag as a topic, we propose alearning-to-rank approach for modelling hashtag relevance,and present methods to extract time-aware features fromhighly dynamic content. We present the data collection andprocessing pipeline, as well as our methodology for achievinglow latency, high precision recommendations. Our empiricalresults show that our method outperforms the state-of-theart,delivering more than 80% precision. Our techniques areimplemented in a real-time system1, and are currently underuser trial with a big news organisation.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Keywords: Machine learningStatisticsLearning-to-rankDynamic topicsSocial indexingNewsHash-tag recommendation
DOI: 10.1145/2872427.2882982
Language: en
Status of Item: Peer reviewed
Is part of: WWW '16 Proceedings of the 25th International Conference on World Wide Web
Conference Details: 25th International World Wide Web Conference, Montreal, Canada, 11 - 15 April 2016
Appears in Collections:Insight Research Collection

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