  In order to support the study of recent advances in recommender systems, this
paper presents an extended recommendation library consisting of eight packages
for up-to-date topics and architectures. First of all, from a data perspective,
we consider three important topics related to data issues (i.e., sparsity, bias
and distribution shift), and develop five packages accordingly: meta-learning,
data augmentation, debiasing, fairness and cross-domain recommendation.
Furthermore, from a model perspective, we develop two benchmarking packages for
Transformer-based and graph neural network (GNN)-based models, respectively.
All the packages (consisting of 65 new models) are developed based on a popular
recommendation framework RecBole, ensuring that both the implementation and
interface are unified. For each package, we provide complete implementations
from data loading, experimental setup, evaluation and algorithm implementation.
This library provides a valuable resource to facilitate the up-to-date research
in recommender systems. The project is released at the link:
https://github.com/RUCAIBox/RecBole2.0.
