Merge pull request #2142 from TayTroye/updateDocs

Update docs

Author: TayTroye

README.md

@@ -24,7 +24,7 @@
 
 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 91 recommendation algorithms, covering four major categories:
+Our library includes 94 recommendation algorithms, covering four major categories:
 
 + General Recommendation
 + Sequential Recommendation
@@ -48,8 +48,8 @@ In order to support the study of recent advances in recommender systems, we cons
 + **General and extensible data structure.** We design general and extensible data structures to unify the formatting and
 usage of various recommendation datasets.
 
-+ **Comprehensive benchmark models and datasets.** We implement 78 commonly used recommendation algorithms, and provide
-the formatted copies of 28 recommendation datasets.
++ **Comprehensive benchmark models and datasets.** We implement 94 commonly used recommendation algorithms, and provide
+the formatted copies of 43 recommendation datasets.
 
 + **Efficient GPU-accelerated execution.** We optimize the efficiency of our library with a number of improved techniques
 oriented to the GPU environment.
@@ -59,9 +59,11 @@ for testing and comparing recommendation algorithms.
 
 
 ## RecBole News
+![new](/asset/new.gif) **02/23/2025**: We release RecBole [v1.2.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.2.1).
+
 ![new](/asset/new.gif) **11/01/2023**: We release RecBole [v1.2.0](https://github.com/RUCAIBox/RecBole/releases/tag/v1.2.0).
 
-![new](/asset/new.gif) **11/06/2022**: We release [the optimal hyperparameters of the model and their tuning ranges](https://recbole.io/hyperparameters/index.html).
+**11/06/2022**: We release [the optimal hyperparameters of the model and their tuning ranges](https://recbole.io/hyperparameters/index.html).
 
 **10/05/2022**: We release RecBole [v1.1.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.1.1).
 
@@ -101,13 +103,13 @@ 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  |            91 models and 43 datasets             |
+|    Models and datasets    |     73 models and 28 datasets      |  65 models and 8 new datasets  |            94 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      |
 |  Hyper-parameter tuning   |       Serial gradient search       |     Serial gradient search     | Three search methods in both serial and parallel |
 |     Significance test     |                 -                  |               -                |               Available interface                |
-|     Benchmark results     |                 -                  | Partially public (GNN and CDR) |      Benchmark configurations on 82 models       |
+|     Benchmark results     |                 -                  | Partially public (GNN and CDR) |      Benchmark configurations on 94 models       |
 |      Friendly usage       |           Documentation            |         Documentation          |       Improved documentation and FAQ page        |
 
 
@@ -249,6 +251,7 @@ We will keep improving our implementations, and update these test results.
 ## RecBole Major Releases
 | Releases | Date       |
 |----------|------------|
+| v1.2.1   | 02/23/2025 |
 | v1.2.0   | 11/01/2023 |
 | v1.1.1   | 10/05/2022 |
 | v1.0.0   | 09/17/2021 |
@@ -293,7 +296,7 @@ We thank the nice contributions through PRs from [@rowedenny](https://github.com
 
 
 ## Cite
-If you find RecBole useful for your research or development, please cite the following papers: [RecBole[1.0]](https://arxiv.org/abs/2011.01731), [RecBole[2.0]](https://dl.acm.org/doi/abs/10.1145/3459637.3482016) and [RecBole[1.2.0]](https://dl.acm.org/doi/10.1145/3539618.3591889).
+If you find RecBole useful for your research or development, please cite the following papers: [RecBole[1.0]](https://arxiv.org/abs/2011.01731), [RecBole[2.0]](https://dl.acm.org/doi/abs/10.1145/3459637.3482016) and [RecBole[1.2.1]](https://dl.acm.org/doi/10.1145/3539618.3591889).
 
 ```bibtex
 @inproceedings{recbole[1.0],
@@ -312,7 +315,7 @@ If you find RecBole useful for your research or development, please cite the fol
   publisher = {{ACM}},
   year      = {2022}
 }
-@inproceedings{recbole[1.2.0],
+@inproceedings{recbole[1.2.1],
   author    = {Lanling Xu and Zhen Tian and Gaowei Zhang and Junjie Zhang and Lei Wang and Bowen Zheng and Yifan Li and Jiakai Tang and Zeyu Zhang and Yupeng Hou and Xingyu Pan and Wayne Xin Zhao and Xu Chen and Ji{-}Rong Wen},
   title     = {Towards a More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems},
   booktitle = {{SIGIR}},
@@ -334,7 +337,8 @@ Here is the list of our lead developers in each development phase. They are the
 | June 2020<br> ~<br> Nov. 2020 |        v0.1.1         |  Shanlei Mu ([@ShanleiMu](https://github.com/ShanleiMu)), Yupeng Hou ([@hyp1231](https://github.com/hyp1231)),<br> Zihan Lin ([@linzihan-backforward](https://github.com/linzihan-backforward)), Kaiyuan Li ([@tsotfsk](https://github.com/tsotfsk))| [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482016) |
 |    Nov. 2020<br> ~ <br> Jul. 2022    | v0.1.2 ~ v1.0.1 |      Yushuo Chen ([@chenyushuo](https://github.com/chenyushuo)), Xingyu Pan ([@2017pxy](https://github.com/2017pxy))    | [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482016)  |
 | Jul. 2022<br/> ~ <br/> Nov. 2023 | v1.1.0 ~ v1.1.1 | Lanling Xu ([@Sherry-XLL](https://github.com/Sherry-XLL)), Zhen Tian ([@chenyuwuxin](https://github.com/chenyuwuxin)), Gaowei Zhang ([@Wicknight](https://github.com/Wicknight)), Lei Wang ([@Paitesanshi](https://github.com/Paitesanshi)), Junjie Zhang ([@leoleojie](https://github.com/leoleojie)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
-| Nov. 2023<br/> ~ <br/> now | v1.2.0 | Bowen Zheng ([@zhengbw0324](https://github.com/zhengbw0324)), Chen Ma ([@Yilu114](https://github.com/Yilu114)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
+| Nov. 2023<br/> ~ <br/> Feb. 2025 | v1.2.0 | Bowen Zheng ([@zhengbw0324](https://github.com/zhengbw0324)), Chen Ma ([@Yilu114](https://github.com/Yilu114)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
+| Feb. 2025<br/> ~ <br/> now | v1.2.1 | Enze Liu ([@BishopLiu](https://github.com/BishopLiu)), Kesha Ou ([@TayTroye](https://github.com/TayTroye)), Bingqian Li ([@Fotiligner](https://github.com/Fotiligner)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
 
 
 ## License

README_CN.md

@@ -24,7 +24,7 @@
 
 
 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91个推荐系统模型，包含常见的推荐系统类别，如:
+我们实现了94个推荐系统模型，包含常见的推荐系统类别，如:
 
 + General Recommendation
 + Sequential Recommendation
@@ -46,17 +46,19 @@ 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。
++ **全面的基准模型和数据集** 我们实现了94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。
 
 + **大规模的标准评测** 我们支持一系列被广泛认可的评估方式来测试和比较不同的推荐算法。
 
 
 ## RecBole 新闻
+![new](/asset/new.gif) **02/23/2025**: 我们发布了 [v1.2.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.2.1).
+
 ![new](/asset/new.gif) **11/01/2023**: 我们发布了 [v1.2.0](https://github.com/RUCAIBox/RecBole/releases/tag/v1.2.0).
 
-![new](/asset/new.gif) **11/06/2022**: 我们公开了[模型的最优超参数及其调参范围](https://recbole.io/hyperparameters/index.html).
+**11/06/2022**: 我们公开了[模型的最优超参数及其调参范围](https://recbole.io/hyperparameters/index.html).
 
 **10/05/2022**: 我们发布了 [v1.1.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.1.1).
 
@@ -225,6 +227,7 @@ NOTE: 我们的测试结果只给出了RecBole库中实现模型的大致时间
 ## RecBole 重要发布
 | Releases | Date       |
 |----------|------------|
+| v1.2.1   | 02/23/2023 |
 | v1.2.0   | 11/01/2023 |
 | v1.1.1   | 10/05/2022 |
 | v1.0.0   | 09/17/2021 |
@@ -269,7 +272,7 @@ NOTE: 我们的测试结果只给出了RecBole库中实现模型的大致时间
 
 
 ## 引用
-如果你觉得 RecBole 对你的科研工作有帮助，请引用我们的论文：[RecBole[1.0]](https://arxiv.org/abs/2011.01731)，[RecBole[2.0]](https://dl.acm.org/doi/abs/10.1145/3459637.3482016) 和 [RecBole[1.2.0]](https://dl.acm.org/doi/10.1145/3539618.3591889)。
+如果你觉得 RecBole 对你的科研工作有帮助，请引用我们的论文：[RecBole[1.0]](https://arxiv.org/abs/2011.01731)，[RecBole[2.0]](https://dl.acm.org/doi/abs/10.1145/3459637.3482016) 和 [RecBole[1.2.1]](https://dl.acm.org/doi/10.1145/3539618.3591889)。
 
 ```bibtex
 @inproceedings{recbole[1.0],
@@ -288,14 +291,15 @@ NOTE: 我们的测试结果只给出了RecBole库中实现模型的大致时间
   publisher = {{ACM}},
   year      = {2022}
 }
-@inproceedings{recbole[1.2.0],
+@inproceedings{recbole[1.2.1],
   author    = {Lanling Xu and Zhen Tian and Gaowei Zhang and Junjie Zhang and Lei Wang and Bowen Zheng and Yifan Li and Jiakai Tang and Zeyu Zhang and Yupeng Hou and Xingyu Pan and Wayne Xin Zhao and Xu Chen and Ji{-}Rong Wen},
   title     = {Towards a More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems},
   booktitle = {{SIGIR}},
   pages     = {2837--2847},
   publisher = {{ACM}},
   year      = {2023}
 }
+
 ```
 
 
@@ -309,7 +313,10 @@ RecBole由 [中国人民大学, 北京邮电大学, 华东师范大学](https://
 | 2020年6月<br> ~<br> 2020年11月 |        v0.1.1         |  牟善磊 ([@ShanleiMu](https://github.com/ShanleiMu)), 侯宇蓬 ([@hyp1231](https://github.com/@hyp1231)),<br> 林子涵 ([@linzihan-backforward](https://github.com/linzihan-backforward)), 李凯元 ([@tsotfsk](https://github.com/tsotfsk))| [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482016) |
 |   2020年11月<br> ~ <br> 2022年7月   | v0.1.2 ~ v1.0.1 |      陈昱硕 ([@chenyushuo](https://github.com/https://github.com/chenyushuo)), 潘星宇 ([@2017pxy](https://github.com/2017pxy))    | [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482016)    |
 | 2022年7月<br/> ~ <br/> 2023年11月 | v1.1.0 ~ v1.1.1 | 徐澜玲 ([@Sherry-XLL](https://github.com/Sherry-XLL)), 田震 ([@chenyuwuxin](https://github.com/chenyuwuxin)), 张高玮 ([@Wicknight](https://github.com/Wicknight)), 王磊 ([@Paitesanshi](https://github.com/Paitesanshi)), 张君杰 ([@leoleojie](https://github.com/leoleojie)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
-| 2023年11月<br/> ~ <br/> 现在 | v1.2.0 | 郑博文 ([@zhengbw0324](https://github.com/zhengbw0324)), 马辰 ([@Yilu114](https://github.com/Yilu114)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
+| 2023年11月<br/> ~ <br/> 2025年2月 | v1.2.0 | 郑博文 ([@zhengbw0324](https://github.com/zhengbw0324)), 马辰 ([@Yilu114](https://github.com/Yilu114)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
+| 2025年2月<br/> ~ <br/> 现在 | v1.2.1 | 刘恩泽 ([@BishopLiu](https://github.com/BishopLiu)), 欧柯杉 ([@TayTroye](https://github.com/TayTroye)), 李炳黔 ([@Fotiligner](https://github.com/Fotiligner)) | [PDF](https://dl.acm.org/doi/10.1145/3539618.3591889) |
+
+
 
 ## 免责声明
 RecBole 基于 [MIT License](./LICENSE) 进行开发，本项目的所有数据和代码只能被用于学术目的。

docs/source/conf.py

@@ -20,11 +20,11 @@
 # -- Project information -----------------------------------------------------
 
 project = "RecBole"
-copyright = "2020-2023, RecBole Contributors"
+copyright = "2020-2025, RecBole Contributors"
 author = "AIBox RecBole group"
 
 # The full version, including alpha/beta/rc tags
-release = "1.2.0"
+release = "1.2.1"
 
 
 # -- General configuration ---------------------------------------------------

docs/source/index.rst

@@ -1,17 +1,17 @@
 .. RecBole documentation master file.
-.. title:: RecBole v1.2.0
+.. title:: RecBole v1.2.1
 .. image:: asset/logo.png
 
 =========================================================
 
-`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/>`_
+`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/>`_ | `v1.2.0 </docs/v1.2.0/>`_
 
 Introduction
 -------------------------
 RecBole is a unified, comprehensive and efficient framework developed based on PyTorch.
 It aims to help the researchers to reproduce and develop recommendation models.
 
-In the lastest release, our library includes 91 recommendation algorithms `[Model List]`_, covering four major categories:
+In the lastest release, our library includes 94 recommendation algorithms `[Model List]`_, covering four major categories:
 
 - General Recommendation
 - Sequential Recommendation
@@ -29,7 +29,7 @@ Features:
 - General and extensible data structure
     We deign general and extensible data structures to unify the formatting and usage of various recommendation datasets.
 - Comprehensive benchmark models and datasets
-    We implement 91 commonly used recommendation algorithms, and provide the formatted copies of 43 recommendation datasets.
+    We implement 94 commonly used recommendation algorithms, and provide the formatted copies of 43 recommendation datasets.
 - Efficient GPU-accelerated execution
     We design many tailored strategies in the GPU environment to enhance the efficiency of our library.
 - Extensive and standard evaluation protocols
@@ -102,7 +102,8 @@ 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 ~ 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>`_
+Nov. 2023 ~ Feb. 2025    v1.2.0            `Bowen Zheng <https://github.com/zhengbw0324>`_, `Chen Ma <https://github.com/Yilu114>`_
+Feb. 2025 ~ Now          v1.2.1            `Enze Liu <https://github.com/BishopLiu>`_,  `Kesha Ou <https://github.com/TayTroye>`_,  `Bingqian Li <https://github.com/Fotiligner>`_
 ======================   ===============   =============================================
 
 License

docs/source/user_guide/model_intro.rst

@@ -1,6 +1,6 @@
 Model Introduction
 =====================
-We implement 91 recommendation models covering general recommendation, sequential recommendation,
+We implement 94 recommendation models covering general recommendation, sequential recommendation,
 context-aware recommendation and knowledge-based recommendation. A brief introduction to these models are as follows:
 
 
@@ -117,6 +117,8 @@ the sequential data. The models of session-based recommendation are also include
    model/sequential/sine
    model/sequential/core
    model/sequential/fearec
+   model/sequential/sasreccpr
+   model/sequential/gru4reccpr
 
 
 Knowledge-based Recommendation

recbole/model/general_recommender/asymknn.py

@@ -3,6 +3,7 @@
 import torch
 from recbole.model.abstract_recommender import GeneralRecommender
 from recbole.utils import InputType, ModelType
+from scipy.sparse import csr_matrix
 
 
 class ComputeSimilarity:
@@ -211,7 +212,7 @@ def __init__(self, config, dataset):
             )
             denominator = factor1.dot(factor2.T) + 1e-6
 
-        self.pred_mat = (nominator / denominator).tolil()
+        self.pred_mat = csr_matrix(nominator / denominator).tolil()
 
         # Apply 'locality of scoring function' via q: f(w) = w^q
         self.pred_mat = self.pred_mat.power(self.q)