Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Enhancing clustering blog documents by utilizing author/reader comments
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Extracting the discussion structure in comments on news-articles
Proceedings of the 9th annual ACM international workshop on Web information and data management
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
Introduction to Information Retrieval
Introduction to Information Retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Predicting the volume of comments on online news stories
Proceedings of the 18th ACM conference on Information and knowledge management
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
User comments for news recommendation in forum-based social media
Information Sciences: an International Journal
Recommender Systems Handbook
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Improving question recommendation by exploiting information need
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
I want to answer; who has a question?: Yahoo! answers recommender system
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
News comments: exploring, modeling, and online prediction
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Personalized recommendation of user comments via factor models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Dynamic personalized recommendation of comment-eliciting stories
Proceedings of the sixth ACM conference on Recommender systems
Topic-driven reader comments summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
Diversifying user comments on news articles
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
The self-feeding process: a unifying model for communication dynamics in the web
Proceedings of the 22nd international conference on World Wide Web
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
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Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight on Web documents and may be utilized for various tasks. This work presents a model that predicts, for a given user, suitable news stories for commenting. The model achieves encouraging results regarding the ability to connect users with stories they are likely to comment on. This provides grounds for personalized recommendations of stories to users who may want to take part in their discussion. We combine a content-based approach with a collaborative-filtering approach (utilizing users' co-commenting patterns) in a latent factor modeling framework. We experiment with several variations of the model's loss function in order to adjust it to the problem domain. We evaluate the results on two datasets and show that employing co-commenting patterns improves upon using content features alone, even with as few as two available comments per story. Finally, we try to incorporate available social network data into the model. Interestingly, the social data does not lead to substantial performance gains, suggesting that the value of social data for this task is quite negligible.