Learning content and usage factors simultaneously to reduce clickbaits

Arnab Bhadury, Aanchan Mohan

Abstract: Recommending news and content is often more difficult than classic recommendation problems. At recommendation time, there is often less high quality explicit usage signals like upvotes, shares, dislikes, etc. because articles are relevant for a very short amount of time. Solely relying on implicit usage signals (views) in collaborative filtering for news articles often yields low quality documents optimized for views and clicks. Traditionally, content based filtering methods such as topic modeling, named entity extraction etc. are often used to counter or mitigate these issues but result in poorer recommendations on their own, and hybrid solutions of ensembles of content and collaborative filtering are difficult to optimize. This talk proposes learning factorized representations of documents using both the content and usage signals simultaneously. Using both signals simultaneously encourages the content and usage signals to act as regularizers for each other. Also, this serves to keep the recommendation quality high while reducing the number of click-baits. This avoids the additional step of tuning often-used ensembled content and collaborative filtering based hybrid models. This research explores learning these shared factorized representations between the two views using the traditional matrix factorization framework as well as probabilistic approaches based on topic modeling. This talk shares the lessons learned from using both approaches and shows the impact of using these learned representations on recommendation quality.