Billion Scale Recommendations at Sharechat and Moj
Content marketplaces (like Sharechat, Moj, Instagram, Tiktok) face unique challenges in recommending content to their users. In addition to traditional end user metrics, these recommender systems will also have to care heavily about fairness and equity of the content creators. The volume of content that is uploaded to these platforms far exceeds the traditional commerce, music or movies use cases by orders of magnitude. For instance, on Moj the number of uploads per hour by our content creators is approximately equal to the total number of content pieces (including individual episodes) on Netflix historically. Making matters harder on short video platforms such as Moj, the average length of a content piece is around 15-20 secs while the average session lasts more than 30 minutes. This means that traditional recommender systems that care purely about getting the top 5-10 recommendations absolutely correct are not going to work very well in this case as we need to continue to maintain relevance and interest well into the top 200-300 recommendations. In this talk, I will give a brief overview of the AI journey at Sharechat and Moj - the number 1 Indian content marketplace platform. I will present some of the research challenges we are solving along with techniques and results that worked for us. I will also talk about some of the key decisions that we took along the way that helped us scale up AI org and its efficiency. The talk will have a mix of technical, research and strategic discussion points in our journey so far.
Hastagiri is the Senior Director of AI at ShareChat, India's largest AI powered content ecosystem driven largely by feed personalisation, automated content understanding and improvements in camera and creator tools!