Reading Summary: "Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles"

Summary

The main research goal of this paper [1] is to recommend a "Comment-Worthy" article list for users. The authors argued the previous research [2] adopted a content-based collaborative filter approach that lack of the consideration of the relation between article segments and comments. They provided an example about cell phone introduction news, the comments listed below might connect to the different segments inside the article, or even the irreverent interests from readers. Hence, it is inappropriate to consider an article and its comments as a single topic. They proposed a Collaborative Correspondence Topic Models (CCTM) approach that can capture the probability distribution between article-comment and article-user, and follow by the Monte Carlo Simulation, Gibbs Sampling, stochastic MCEM algorithm to estimate the latent offsets and generated the perdition list. According to the paper, the experiment indicated the performance is better than three baseline models in both cold-start and warm-start settings basis. 

Thoughts

The idea to consider the relation between article-comment and article-user is interesting. The authors take the user profiling (based on the user comments) into account, to build up a user-personalized comment-worthy recommendation list. This paper provided a way to link the user feedback and their user interests. This could be a way to build up the user profiling (modeling). The idea in this paper could be applied to the person's recommendation as well. However, I have a couple comments on this paper: 1) the empirical result pointed a clean dataset difference. The model performance of the Daily Mail is much better than the other two. There might be some characteristic exist in the dataset that make the model can do a better prediction task; 2) the commenting behavior is diversity. Many of the comments are actually junk posts. I think an exploration of the dataset comment distribution would be better to understand the commenting behavior; 3) the proposed is adopted content-based and topic model approaches. We can only see a slight improvement between the new proposed model and the CoTM (pure content-based approach). 

Some possible potential research topics: 

• Inline commenting behavior: what is the user replying to? For any real world events or the articles, news, social network posts. There are tons of the re-ply posts from the public readers or their social network friends. It will be interesting to see the re-play behavior and analysis in different scenarios. For example, when a new apple product announcement news published, what is the comment mainly about? Or, when an emotional (happy, sad, exciting, etc.) tweets showed on Twitter, what is the response from their following/followers? 
• Content Driven User Modeling: we might take the published text or other user generated content to build up a user model. This model could apply to recommendation task, behavior comparison, performance analysis, etc. It will be interesting if we can leverage the current information to the other targets, e.g. to solve the cold-start problem or learn from the other rich data source. 

More...

• The idea about use sampling to inference the data distribution from machine learning perspective. E.g. the Markov chain Monte Carlo (MCMC), Gibbs sampling. 
• Topic modeling approaches, e.g. CORR-LDA, Collaborative Topic Modeling

Reference

1. Bansal, T., Das, M., & Bhattacharyya, C. (2015, September). Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 195-202). ACM.
2. Shmueli, E., Kagian, A., Koren, Y., & Lempel, R. (2012, April). Care to comment?: recommendations for commenting on news stories. In Proceedings of the 21st international conference on World Wide Web (pp. 429-438). ACM. ISO 690