FIX:update readme.md

README.md

@@ -24,14 +24,14 @@
 
 RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified,
 comprehensive and efficient framework for research purpose.
-Our library includes 78 recommendation algorithms, covering four major categories:
+Our library includes 91 recommendation algorithms, covering four major categories:
 
 + General Recommendation
 + Sequential Recommendation
 + Context-aware Recommendation
 + Knowledge-based Recommendation
 
-We design a unified and flexible data file format, and provide the support for 28 benchmark recommendation datasets.
+We design a unified and flexible data file format, and provide the support for 43 benchmark recommendation datasets.
 A user can apply the provided script to process the original data copy, or simply download the processed datasets
 by our team.
 
@@ -101,7 +101,7 @@ These extensions make it much easier to reproduce the benchmark results and stay
 |          Aspect           |            RecBole 1.0             |          RecBole 2.0           |                   This update                    |
 | :-----------------------: | :--------------------------------: | :----------------------------: | :----------------------------------------------: |
 |   Recommendation tasks    |            4 categories            |    3 topics and 5 packages     |                   4 categories                   |
-|    Models and datasets    |     73 models and 28 datasets      |  65 models and 8 new datasets  |            86 models and 41 datasets             |
+|    Models and datasets    |     73 models and 28 datasets      |  65 models and 8 new datasets  |            91 models and 43 datasets             |
 |      Data structure       | Implemented Dataset and Dataloader |         Task-oriented          |  Compatible data module inherited from PyTorch   |
 |    Continuous features    |          Field embedding           |        Field embedding         |        Field embedding and discretization        |
 | GPU-accelerated execution |       Single-GPU utilization       |     Single-GPU utilization     |      Multi-GPU and mixed precision training      |

README_CN.md

@@ -24,15 +24,15 @@
 
 
 RecBole 是一个基于 PyTorch 实现的，面向研究者的，易于开发与复现的，统一、全面、高效的推荐系统代码库。
-我们实现了78个推荐系统模型，包含常见的推荐系统类别，如:
+我们实现了91个推荐系统模型，包含常见的推荐系统类别，如:
 
 + General Recommendation
 + Sequential Recommendation
 + Context-aware Recommendation
 + Knowledge-based Recommendation
 
 
-我们约定了一个统一、易用的数据文件格式，并已支持 28 个 benchmark dataset。
+我们约定了一个统一、易用的数据文件格式，并已支持 43 个 benchmark dataset。
 用户可以选择使用我们的数据集预处理脚本，或直接下载已被处理好的数据集文件。
 
 
@@ -46,7 +46,7 @@ RecBole 是一个基于 PyTorch 实现的，面向研究者的，易于开发与
 ## 特色
 + **通用和可扩展的数据结构** 我们设计了通用和可扩展的数据结构来支持各种推荐数据集统一化格式和使用。
 
-+ **全面的基准模型和数据集** 我们实现了78个常用的推荐算法，并提供了28个推荐数据集的格式化副本。
++ **全面的基准模型和数据集** 我们实现了91个常用的推荐算法，并提供了43个推荐数据集的格式化副本。
 
 + **高效的 GPU 加速实现** 我们针对GPU环境使用了一系列的优化技术来提升代码库的效率。
 

docs/source/index.rst

@@ -4,7 +4,7 @@
 
 =========================================================
 
-`HomePage <https://recbole.io/>`_ | `Docs <https://recbole.io/docs/>`_ | `GitHub <https://github.com/RUCAIBox/RecBole>`_ | `Datasets <https://github.com/RUCAIBox/RecDatasets>`_ | `v0.1.2 </docs/v0.1.2/>`_ | `v0.2.0 </docs/v0.2.0/>`_ | `v1.0.0 </docs/v1.0.0/>`_ | `v1.0.1 </docs/v1.0.1/>`_ | `v1.2.0 </docs/v1.2.0/>`_
+`HomePage <https://recbole.io/>`_ | `Docs <https://recbole.io/docs/>`_ | `GitHub <https://github.com/RUCAIBox/RecBole>`_ | `Datasets <https://github.com/RUCAIBox/RecDatasets>`_ | `v0.1.2 </docs/v0.1.2/>`_ | `v0.2.0 </docs/v0.2.0/>`_ | `v1.0.0 </docs/v1.0.0/>`_ | `v1.0.1 </docs/v1.0.1/>`_ | `v1.1.1 </docs/v1.1.1/>`_
 
 Introduction
 -------------------------
@@ -101,7 +101,8 @@ Time                     Version 	        Lead Developers
 ======================   ===============   =============================================
 June 2020 ~ Nov. 2020    v0.1.1            `Shanlei Mu <https://github.com/ShanleiMu>`_, `Yupeng Hou <https://github.com/hyp1231>`_, `Zihan Lin <https://github.com/linzihan-backforward>`_, `Kaiyuan Li <https://github.com/tsotfsk>`_
 Nov. 2020 ~ Oct. 2022    v0.1.2 ~ v1.0.1   `Yushuo Chen <https://github.com/chenyushuo>`_, `Xingyu Pan <https://github.com/2017pxy>`_
-Oct. 2022 ~ Now          v1.1.0 ~ v1.1.1   `Lanling Xu <https://github.com/Sherry-XLL>`_, `Zhen Tian <https://github.com/chenyuwuxin>`_, `Gaowei Zhang <https://github.com/Wicknight>`_, `Lei Wang <https://github.com/Paitesanshi>`_, `Junjie Zhang <https://github.com/leoleojie>`_
+Oct. 2022 ~ Nov. 2023    v1.1.0 ~ v1.1.1   `Lanling Xu <https://github.com/Sherry-XLL>`_, `Zhen Tian <https://github.com/chenyuwuxin>`_, `Gaowei Zhang <https://github.com/Wicknight>`_, `Lei Wang <https://github.com/Paitesanshi>`_, `Junjie Zhang <https://github.com/leoleojie>`_
+Nov. 2023 ~ now          v1.2.0            `Bowen Zheng <https://github.com/zhengbw0324>`_, `Chen Ma <https://github.com/Yilu114>`_
 ======================   ===============   =============================================
 
 License