To rank content for sparse users:
This approach uses real-time predictions, caching dense user weights for efficiency. We used a multi-armed bandit (MAB) model to provide exploration and discovery within recommendations. The MAB would identify the slots, here categories, that we need. And depending on the category, we would fill the slots with either popularity or recency-ranked items of that category. Some categories work better with recency, like news, while others work better with popularity, like fashion - this we had established as part of a prior analysis.
These results of the A/B experiment highlight the effectiveness of the clustering-based recommendation system in improving engagement. Further iterations and optimizations are ongoing, but the initial success is promising.
So we wrote a paper about it :)
For a detailed study, refer to the paper.
For more information, contact me or Ritika.
Cheers.