Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling
Anushya Subbiah, Steffen Rendle, Vikram Aggarwal · 2024
In recommendation systems, there has been a growth in the number of recommendable items (# of movies, music, products). When the set of recommendable items is large, training and evaluation of item recommendation models becomes computationally expensive. To lower this cost, it has become common to sample negative items. However, the recommendation quality can suffer from biases introduced by traditional negative sampling mechanisms.