Catching up with developments in Recommendation Algorithms using Deep Learning

In my quest to identify technology and business gaps for Voicy.AI, I have been spending time to catch up with developments in Recommendation Algorithms using Deep Learning. I started my research reading Recommendations paper from YouTube. Recommendation in general is a two step process consisting of Retrieval and Re-Ranking. The authors have phrased the retrieval as a Multi Class classification instead of reusing Inverted Index scoring mechanisms. I liked the tricks of Negative Sampling and Sub-Linear scoring using hashing techniques to optimize for training and serving in production respectively. I than moved to another important development in the recommendation systems leveraging joint training of Wide And Deep Learning Neural Nets pioneered by Apps team at Google. I was impressed with the observation of authors about how Wide Model is good for Memorization and Deep Model is good for generalization.

I than stumbled upon another research paper from UCL folks. The authors have focused on the retrieval problem of recommendations in the context of journalism. I liked how the authors have used structure of the problem and seperate attention models to construct profiles to predict recommendations. It was impressive to see the big leaps by DL algorithms for Recommendation problem from Collaborative Filtering algorithms few years back.

What is your opinion about the next DL paradigm for recommendations? Any suggestions for more popular research papers in DL based recommendations?