Do you know your customers? That’s a perennial question that drives a large portion of the marketing investment across industries, keeping CMOs awake. Analytics is key to driving insights and inform user-level actionable decisions.
This challenge is to predict granular customer/item affinity to support a better decision-making process. In a broader application, this information can drive front-line results in Marketin Campaign or Online Experience Optimization.
Traditional approaches usually fail because features do not hold much predictive power. Indeed, most of it resides in the sequence of ratings from users, more than in the user and item descriptions. We pushed the challenge to the extreme where no user or item metadata is provided.
Each customer has rated items from 1 (= strongly dislike) to 5 (strongly like). The task is to predict as accurately as possible (measured by the RMSE) the affinity between customers and items in the scoring dataset. Good luck!
Data Description: You are provided with 2 files: a training set (.train) and a scoring set (.score). Both are space delimited files.
This challenge is to predict granular customer/item affinity to support a better decision-making process. In a broader application, this information can drive front-line results in Marketin Campaign or Online Experience Optimization.
Traditional approaches usually fail because features do not hold much predictive power. Indeed, most of it resides in the sequence of ratings from users, more than in the user and item descriptions. We pushed the challenge to the extreme where no user or item metadata is provided.
Each customer has rated items from 1 (= strongly dislike) to 5 (strongly like). The task is to predict as accurately as possible (measured by the RMSE) the affinity between customers and items in the scoring dataset. Good luck!
Data Description: You are provided with 2 files: a training set (.train) and a scoring set (.score). Both are space delimited files.