Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation
Yile Liang, Tieyun Qian, Qing Li, Hongzhi Yin · 2021
Recommender systems are playing a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key factor to broaden users' horizons as well as to promote enterprises' sales. However, the trade-off between accuracy and diversity remains to be a big challenge. More importantly, none of existing methods has explored the domain and user biases toward diversity.