Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
User comments for news recommendation in social media
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Personalized recommendation of user comments via factor models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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On a news website, an article may receive thousands of comments from its readers on a variety of topics. The usual display of these comments in a ranked list, e.g. by popularity, does not allow the user to follow discussions on a particular topic. Organizing them by semantic topics enables the user not only to selectively browse comments on a topic, but also to discover other significant topics of discussion in comments. This topical organization further allows to explicitly capture the immediate interests of the user even when she is not logged in. Here we use this information to recommend content that is relevant in the context of the comments being read by the user. We present an algorithm for building such a topical organization in a practical setting and study different recommendation schemes. In a pilot study, we observe these comments-to-article recommendations to be preferred over the standard article-to-article recommendations.