Collaborative Filtering via Additive Ordinal Regression
Jun Hu, Ping Li · 2018
Accurately predicting user preferences/ratings over items are crucial for many Internet applications, e.g., recommender systems, online advertising. In current main-stream algorithms regarding the rating prediction problem, discrete rating scores are often viewed as either numerical values or(nominal) categorical labels. Practically, viewing user rating scores as numerical values or categorical labels cannot precisely reflect the exact degree of user preferences. It is expected that for each user, the quantitative distance/scale between any pair of adjacent rating scores could be different.