Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles
By Trapit Bansal, Mrinal Kanti Das, C. Bhattacharyya. 2015
89 citations recorded by the source corpus.
We consider the problem of recommending comment-worthy articles 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 difficulty 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 resolved by leveraging article and comment content through topic modeling and the co-commenting pattern of users through collaborative filtering, combined within a novel hierarchical Bayesian modeling approach. Our solution, Collaborative Correspondence Topic Models (CCTM), generates user profiles which are leveraged to provide a personalized ranking of comment-worthy articles 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 problem 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 comments). 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.
Subfields: Artificial Intelligence, Information Systems
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Citations in the corpus
- DKN: Deep Knowledge-Aware Network for News Recommendation (2018, background): “To alleviate the impact of information over-loading, it is critical to help users target their reading interests and make personalized recommendations [2, 25, 27, 32, 34, 39].”
- Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “Examples of text recommendations include recommending blogs, social media posts [1], news articles [2, 3], movies (based on plot summaries), products (based on reviews) [4] and research papers [5].”
- News recommender systems - Survey and roads ahead (2018, background): “[8] 2015 H Cold-Start ArsTechnica Gadgets, AT-Science, DailyMail AUC, Hit Rate Offline”
- Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (2017, background): “One needs a general model to capture such underlying paerns, which is an open research topic in natural language processing [19] and content-based recommendation [6, 30].”
- Neural News Recommendation with Long- and Short-term User Representations (2019, background): “, 2014; Bansal et al., 2015; Lian et al., 2018). For example, Liu et al. (2010) proposed to use the topic categories and interests features predicted by a Bayesian model to represent news, and use the click distribution features of news categories to represent users.”
- MIND: A Large-scale Dataset for News Recommendation (2020, background): “Traditional news recommendation meth-ods usually rely on feature engineering to represent news articles and user interest (Liu et al., 2010; 3 https://microsoftnews.msn.com/ Son et al., 2013; Karkali et al., 2013; Garcin et al., 2013; Bansal et al., 2015; Chen et al., 2017).”