add missing doc

Author: pintonos

docs/source/asset/trimlp.png

@@ -11,7 +11,7 @@ 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 94 recommendation algorithms `[Model List]`_, covering four major categories:
+In the lastest release, our library includes 95 recommendation algorithms `[Model List]`_, covering four major categories:
 
 - General Recommendation
 - Sequential Recommendation

docs/source/index.rst

@@ -0,0 +1,79 @@
+TriMLP
+===========
+
+Introduction
+---------------------
+
+`[paper] <https://dl.acm.org/doi/10.1145/3670995>`_
+
+**Title:** TriMLP: A Foundational MLP-Like Architecture for Sequential Recommendation
+
+**Authors:** Yiheng Jiang, Yuanbo Xu, Yongjian Yang, Funing Yang, Pengyang Wang, Chaozhuo Li, Fuzhen Zhuang, Hui Xiong
+
+**Abstract:**  In this work, we present TriMLP as a foundational MLP-like architecture for the sequential recommendation, simultaneously achieving computational efficiency and promising performance. First, we empirically study the incompatibility between existing purely MLP-based models and sequential recommendation, that the inherent fully-connective structure endows historical user–item interactions (referred as tokens) with unrestricted communications and overlooks the essential chronological order in sequences. Then, we propose the MLP-based Triangular Mixer to establish ordered contact among tokens and excavate the primary sequential modeling capability under the standard auto-regressive training fashion. It contains (1) a global mixing layer that drops the lower-triangle neurons in MLP to block the anti-chronological connections from future tokens and (2) a local mixing layer that further disables specific upper-triangle neurons to split the sequence as multiple independent sessions. The mixer serially alternates these two layers to support fine-grained preferences modeling, where the global one focuses on the long-range dependency in the whole sequence, and the local one calls for the short-term patterns in sessions. Experimental results on 12 datasets of different scales from 4 benchmarks elucidate that TriMLP consistently attains favorable accuracy/efficiency tradeoff over all validated datasets, where the average performance boost against several state-of-the-art baselines achieves up to 14.88%, and the maximum reduction of inference time reaches 23.73%. The intriguing properties render TriMLP a strong contender to the well-established RNN-, CNN-, and Transformer-based sequential recommenders. Code is available at https://github.com/jiangyiheng1/TriMLP.
+
+.. image:: ../../../asset/trimlp.png
+    :width: 500
+    :align: center
+
+Running with RecBole
+-------------------------
+
+**Model Hyper-Parameters:**
+
+- ``embedding_size (int)`` : The embedding size of items. Defaults to ``64``.
+- ``act_fct (float)`` : The activation function in feed-forward layer. Defaults to ``'None'``. Range in ``['None', 'tanh', 'sigmoid']``.
+- ``num_session (float)`` : The number of sessions per sequence. Defaults to ``2``.
+- ``dropout_prob (float)`` : The dropout rate. Defaults to ``0.5``.
+- ``loss_type (str)`` : The type of loss function. Is fixed to ``'CE'``.
+
+
+
+**A Running Example:**
+
+Write the following code to a python file, such as `run.py`
+
+.. code:: python
+
+   from recbole.quick_start import run_recbole
+
+   parameter_dict = {
+      'train_neg_sample_args': None,
+   }
+   run_recbole(model='TriMLP', dataset='ml-100k', config_dict=parameter_dict)
+
+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
+
+   learning_rate choice [0.01, 0.005, 0.001, 0.0005, 0.0001]
+   act_fct choice ['None', 'tanh', 'sigmoid']
+   dropout_prob choice [0.2, 0.5]
+   num_session choice [1, 2, 3, 4, 8, 16, 32]
+
+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`

docs/source/user_guide/model/sequential/trimlp.rst

@@ -29,3 +29,4 @@
 from recbole.model.sequential_recommender.stamp import STAMP
 from recbole.model.sequential_recommender.transrec import TransRec
 from recbole.model.sequential_recommender.fearec import FEARec
+from recbole.model.sequential_recommender.trimlp import TriMLP

recbole/model/sequential_recommender/__init__.py

@@ -82,7 +82,6 @@ def __init__(self, config, dataset):
             self.act_fct = None
 
         self.dropout_prob = config["dropout_prob"]
-        self.final_softmax = config["final_softmax"]
         self.num_session = config["num_session"]
 
         # define layers and loss

recbole/model/sequential_recommender/trimlp.py

@@ -780,6 +780,13 @@ def test_fea_rec(self):
         }
         quick_test(config_dict)
 
+    def test_trimlp(self):
+        config_dict = {
+            "model": "TriMLP",
+            "train_neg_sample_args": None,
+        }
+        quick_test(config_dict)
+
     # def test_gru4reckg(self):
     #     config_dict = {
     #         'model': 'GRU4RecKG',