Ad recommendation models are typically built on historical user-to-ads interaction data such as impressions, clicks, etc. Only the data from user-to-ads activities is used. However, the result of this approach may not be satisfactory to predict user-to-ad behaviours and make it less likely to recommend relevant ads to the users.
You are asked to enhance ads click-through rate (CTR) prediction accuracy by leveraging ad logs, user profiles, and user behaviour with the newsfeed.
Can you optimise your abilities?
