  With the applications of recommendation systems rapidly expanding, an
increasing number of studies have focused on every aspect of recommender
systems with different data inputs, models, and task settings. Therefore, a
flexible library is needed to help researchers implement the experimental
strategies they require. Existing open libraries for recommendation scenarios
have enabled reproducing various recommendation methods and provided standard
implementations. However, these libraries often impose certain restrictions on
data and seldom support the same model to perform different tasks and input
formats, limiting users from customized explorations. To fill the gap, we
propose ReChorus2.0, a modular and task-flexible library for recommendation
researchers. Based on ReChorus, we upgrade the supported input formats, models,
and training&evaluation strategies to help realize more recommendation tasks
with more data types. The main contributions of ReChorus2.0 include: (1)
Realization of complex and practical tasks, including reranking and CTR
prediction tasks; (2) Inclusion of various context-aware and rerank
recommenders; (3) Extension of existing and new models to support different
tasks with the same models; (4) Support of highly-customized input with
impression logs, negative items, or click labels, as well as user, item, and
situation contexts. To summarize, ReChorus2.0 serves as a comprehensive and
flexible library better aligning with the practical problems in the
recommendation scenario and catering to more diverse research needs. The
implementation and detailed tutorials of ReChorus2.0 can be found at
https://github.com/THUwangcy/ReChorus.
