Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles

Trapit Bansal, Mrinal Das, Chiranjib Bhattacharyya

Abstract:

We consider the problem of recommending comment-worthy ar
ticles such as news and blog-posts. An article is defined to be
comment-worthy for a particular user if that user is interested to
leave a comment on it. We note that recommending comment
worthy articles calls for elicitation of commenting-interests of the
user from the content of both the articles and the past comments
made by users. We thus propose to develop content-driven user
profiles to elicit these latent interests of users in commenting and
use them to recommend articles for future commenting. The diffi
culty of modeling comment content and the varied nature of users’
commenting interests make the problem technically challenging.
The problem of recommending comment-worthy articles is re
solved by leveraging article and comment content through topic
modeling and the co-commenting pattern of users through collab
orative filtering, combined within a novel hierarchical Bayesian
modeling approach. Our solution, Collaborative Correspondence
Topic Models (CCTM), generates user profiles which are lever
aged to provide a personalized ranking of comment-worthy arti
cles for each user. Through these content-driven user profiles,
CCTM effectively handle the ubiquitous problem of cold-start
without relying on additional meta-data. The inference prob
lem for the model is intractable with no off-the-shelf solution and
we develop an efficient Monte Carlo EM algorithm. CCTM is
evaluated on three real world data-sets, crawled from two blogs,
ArsTechnica (AT) Gadgets (102,087 comments) and AT-Science
(71,640 comments), and a news site, DailyMail (33,500 com
ments). We show average improvement of 14% (warm-start)
and 18% (cold-start) in AUC, and 80% (warm-start) and 250%
(cold-start) in Hit-Rank@5, over state of the art [1, 2]