FEA: Add FPSR

docs/source/user_guide/model/general/fpsr.rst

@@ -0,0 +1,181 @@
+FPSR
+===========
+
+Introduction
+---------------------
+
+`[paper] <https://arxiv.org/abs/2207.05959>`_
+
+**Title:** Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
+
+**Authors:** Tianjun Wei, Jianghong Ma, Tommy W. S. Chow
+
+**Abstract:**  Collaborative filtering (CF) is widely searched in recommendation with various types of 
+solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of 
+modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch 
+optimization limit their efficiency. Instead, item similarity models attempt to construct direct 
+relationships through efficient interaction encoding. Despite their great performance, the growing item 
+numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. 
+In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency 
+improving, and identify the potential item group structure in the sampled graph. Based on this, we propose 
+a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling 
+within each partition. Specifically, we show that the spectral information of the original graph is well 
+in preserving global-level information. Then, it is added to fine-tune local item similarities with a new 
+data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information 
+loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model 
+outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95% parameter 
+storage savings.
+
+.. image:: ../../../asset/fpsr.png
+    :width: 500
+    :align: center
+
+Running with RecBole
+-------------------------
+
+**Model Hyper-Parameters:**
+
+- ``eigenvectors (int)`` : The number of top eigenvectors of item-item adjacency matrix. Defaults to ``256``.
+- ``lambda (float)`` : Control the relative weights of global-level information and partition-aware item similarities. Range in ``[0, 1]``. Defaults to ``0.5``.
+- ``rho (float)`` : The penalty parameter that applies to the squared difference between primal variables. Defaults to ``5000.0``.
+- ``theta_1 (float)`` : L1-norm regularization parameter. Defaults to ``0.1``.
+- ``theta_2 (float)`` : L2-norm regularization parameter. Defaults to ``1.0``.
+- ``eta (float)`` : The weight for intra-partition data augmentation. Defaults to ``1.0``.
+- ``tol (float)`` : The threshold to filter out values in the learned partition-aware similarity matrix. Defaults to ``5.0e-3``.
+- ``tau (float)`` : The size ratio of the item-item graph partitioning. Range in ``[0, 1]``. Defaults to ``0.2``.
+
+
+**A Running Example:**
+
+Write the following code to a python file, such as `run.py`
+
+.. code:: python
+
+  from recbole.quick_start import run_recbole
+
+  run_recbole(model='FPSR', dataset='ml-100k')
+
+And then:
+
+.. code:: bash
+
+  python run.py
+
+Tuning Hyper Parameters
+-------------------------
+
+If you want to use ``HyperTuning`` to tune hyper parameters of this model, you can copy the following settings and name it as ``hyper.test``.
+
+.. code:: bash
+
+  lambda choice [0.0,0.1,0.2,0.3,0.4,0.5]
+  theta_2 choice [1e-3,1e-2,1e-1,1.0]
+  eta choice [1e-3,1e-2,1e-1,1.0]
+
+Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.
+
+Then, with the source code of RecBole (you can download it from GitHub), you can run the ``run_hyper.py`` to tuning:
+
+.. code:: bash
+
+	python run_hyper.py --model=[model_name] --dataset=[dataset_name] --config_files=[config_files_path] --params_file=hyper.test
+
+For more details about Parameter Tuning, refer to :doc:`../../../user_guide/usage/parameter_tuning`.
+
+
+If you want to change parameters, dataset or evaluation settings, take a look at
+
+- :doc:`../../../user_guide/config_settings`
+- :doc:`../../../user_guide/data_intro`
+- :doc:`../../../user_guide/train_eval_intro`
+- :doc:`../../../user_guide/usage`FPSR
+===========
+
+Introduction
+---------------------
+
+`[paper] <https://arxiv.org/abs/2207.05959>`_
+
+**Title:** Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
+
+**Authors:** Tianjun Wei, Jianghong Ma, Tommy W. S. Chow
+
+**Abstract:**  Collaborative filtering (CF) is widely searched in recommendation with various types of 
+solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of 
+modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch 
+optimization limit their efficiency. Instead, item similarity models attempt to construct direct 
+relationships through efficient interaction encoding. Despite their great performance, the growing item 
+numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. 
+In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency 
+improving, and identify the potential item group structure in the sampled graph. Based on this, we propose 
+a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling 
+within each partition. Specifically, we show that the spectral information of the original graph is well 
+in preserving global-level information. Then, it is added to fine-tune local item similarities with a new 
+data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information 
+loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model 
+outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95% parameter 
+storage savings.
+
+.. image:: ../../../asset/fpsr.png
+    :width: 500
+    :align: center
+
+Running with RecBole
+-------------------------
+
+**Model Hyper-Parameters:**
+
+- ``eigenvectors (int)`` : The number of top eigenvectors of item-item adjacency matrix. Defaults to ``256``.
+- ``lambda (float)`` : Control the relative weights of global-level information and partition-aware item similarities. Range in ``[0, 1]``. Defaults to ``0.5``.
+- ``rho (float)`` : The penalty parameter that applies to the squared difference between primal variables. Defaults to ``5000.0``.
+- ``theta_1 (float)`` : L1-norm regularization parameter. Defaults to ``0.1``.
+- ``theta_2 (float)`` : L2-norm regularization parameter. Defaults to ``1.0``.
+- ``eta (float)`` : The weight for ntra-partition data augmentation. Defaults to ``1.0``.
+- ``tol (float)`` : The threshold to filter out values in the learned partition-aware similarity matrix. Defaults to ``5.0e-3``.
+- ``tau (float)`` : The size ratio of the item-item graph partitioning. Range in ``[0, 1]``. Defaults to ``0.2``.
+
+
+**A Running Example:**
+
+Write the following code to a python file, such as `run.py`
+
+.. code:: python
+
+  from recbole.quick_start import run_recbole
+
+  run_recbole(model='FPSR', dataset='ml-100k')
+
+And then:
+
+.. code:: bash
+
+  python run.py
+
+Tuning Hyper Parameters
+-------------------------
+
+If you want to use ``HyperTuning`` to tune hyper parameters of this model, you can copy the following settings and name it as ``hyper.test``.
+
+.. code:: bash
+
+  lambda choice [0.0,0.1,0.2,0.3,0.4,0.5]
+  theta_2 choice [1e-3,1e-2,1e-1,1.0]
+  eta choice [1e-3,1e-2,1e-1,1.0]
+
+Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.
+
+Then, with the source code of RecBole (you can download it from GitHub), you can run the ``run_hyper.py`` to tuning:
+
+.. code:: bash
+
+	python run_hyper.py --model=[model_name] --dataset=[dataset_name] --config_files=[config_files_path] --params_file=hyper.test
+
+For more details about Parameter Tuning, refer to :doc:`../../../user_guide/usage/parameter_tuning`.
+
+
+If you want to change parameters, dataset or evaluation settings, take a look at
+
+- :doc:`../../../user_guide/config_settings`
+- :doc:`../../../user_guide/data_intro`
+- :doc:`../../../user_guide/train_eval_intro`
+- :doc:`../../../user_guide/usage`

recbole/model/general_recommender/__init__.py

@@ -6,6 +6,7 @@
 from recbole.model.general_recommender.ease import EASE
 from recbole.model.general_recommender.enmf import ENMF
 from recbole.model.general_recommender.fism import FISM
+from recbole.model.general_recommender.fpsr import FPSR
 from recbole.model.general_recommender.gcmc import GCMC
 from recbole.model.general_recommender.itemknn import ItemKNN
 from recbole.model.general_recommender.lightgcn import LightGCN