Topical organization of user comments and application to content recommendation

Vidit Jain (1) , Esther Galbrun (2, 3)

1 Yahoo! Labs - Yahoo! Labs
2 HIIT - Helsinki Institute for Information Technology
3 Helsingin yliopisto = Helsingfors universitet = University of Helsinki

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.

Mots clés: H4 [Information Systems Applications]: Miscellaneous Keywords Algorithm; User Generated Content; Recommendation

Domaines: Intelligence artificielle [cs.AI]

Vidit Jain, Esther Galbrun. Topical organization of user comments and application to content recommendation. International Conference on World Wide Web, May 2013, Rio de Janeiro, Brazil. ⟨10.1145/2487788.2487812⟩. ⟨hal-01399275⟩