Comments-oriented document summarization: understanding documents with readers' feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to model relatedness for news recommendation
Proceedings of the 20th international conference on World wide web
Improved video categorization from text metadata and user comments
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Information Retrieval in the Commentsphere
ACM Transactions on Intelligent Systems and Technology (TIST)
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Most of previous work on news relatedness focuses on news article texts. In this paper, we study the benefit of user-generated comments on modeling news relatedness. Comments contain rich text information which is provided by commenters and rated by readers with thumb-up or thumb-down, but the quality of individual comments varies widely. We compare different ways of capturing relatedness by leveraging both text and user interaction information in comments. Our evaluation based on an editorial data set demonstrates that the text information in comments is very effective to model relatedness while community rating is quite predictive of the comment quality.