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 # Video files - compressed
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 *.webm filter=lfs diff=lfs merge=lfs -text
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+dataset/iFashion/train.txt filter=lfs diff=lfs merge=lfs -text
+dataset/ml-1M/ratings.dat filter=lfs diff=lfs merge=lfs -text
+dataset/yelp2018/train.txt filter=lfs diff=lfs merge=lfs -text
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+test/MNIST/data/MNIST/raw/t10k-images-idx3-ubyte filter=lfs diff=lfs merge=lfs -text
+test/MNIST/data/MNIST/raw/train-images-idx3-ubyte filter=lfs diff=lfs merge=lfs -text

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README.md

@@ -0,0 +1,202 @@
+<h2>Development</h2>
+
+<h>LightGCN_1110.py</h>
+
+
+能够同时mixup多个samples，而且权重自适应分配。
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<h2>Requirements</h2>
+	
+```
+numba==0.53.1
+numpy==1.20.3
+scipy==1.6.2
+tensorflow==1.14.0
+torch>=1.7.0
+```
+
+<h2>Usage</h2>
+<ol>
+<li>Configure the xx.conf file in the directory named conf. (xx is the name of the model you want to run)</li>
+<li>Run main.py and choose the model you want to run.</li>
+</ol>
+
+<h2>Implemented Models</h2>
+
+<table class="table table-hover table-bordered">
+  <tr>
+		<th>Model</th> 		<th>Paper</th>      <th>Type</th>   <th>Code</th>
+   </tr>
+   <tr>
+    <td scope="row">XSimGCL</td>
+        <td>Yu et al. <a href="https://arxiv.org/abs/2209.02544" target="_blank">XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation</a>, Submitted to TKDE.
+         </td> <td>Graph + CL</d> <td>PyTorch</d> 
+      </tr>
+   <tr>
+    <td scope="row">SimGCL</td>
+        <td>Yu et al. <a href="https://arxiv.org/abs/2112.08679" target="_blank">Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation</a>, SIGIR'22.
+         </td> <td>Graph + CL</d> <td>PyTorch</d> 
+      </tr>
+   <tr>
+    <td scope="row">DirectAU</td>
+        <td>Wang et al. <a href="https://arxiv.org/abs/2206.12811" target="_blank">Towards Representation Alignment and Uniformity in Collaborative Filtering</a>, KDD'22.
+         </td> <td>Graph</d> <td>PyTorch</d> 
+      </tr>   
+<tr>
+    <td scope="row">NCL</td>
+        <td>Lin et al. <a href="https://arxiv.org/abs/2202.06200" target="_blank">Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning</a>, WWW'22.
+         </td> <td>Graph + CL</d> <td>PyTorch</d> 
+      </tr>
+   <tr>
+    <td scope="row">MixGCF</td>
+        <td>Huang et al. <a href="https://keg.cs.tsinghua.edu.cn/jietang/publications/KDD21-Huang-et-al-MixGCF.pdf" target="_blank">MixGCF: An Improved Training Method for Graph Neural
+Network-based Recommender Systems</a>, KDD'21.
+         </td> <td>Graph + DA</d> <td>PyTorch</d> 
+      </tr>
+     <tr>
+    <td scope="row">MHCN</td>
+        <td>Yu et al. <a href="https://dl.acm.org/doi/abs/10.1145/3442381.3449844" target="_blank">Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation</a>, WWW'21.
+         </td> <td>Graph + CL</d> <td>TensorFlow</d>
+      </tr>
+     <tr>	
+    <td scope="row">SGL</td>
+        <td>Wu et al. <a href="https://dl.acm.org/doi/10.1145/3404835.3462862" target="_blank">Self-supervised Graph Learning for Recommendation</a>, SIGIR'21.
+         </td> <td>Graph + CL</d> <td>TensorFlow & Torch</d> 
+      </tr>
+    <tr>
+    <td scope="row">SEPT</td>
+        <td>Yu et al. <a href="https://arxiv.org/abs/2106.03569" target="_blank">Socially-Aware Self-supervised Tri-Training for Recommendation</a>, KDD'21.
+         </td> <td>Graph + CL</d> <td>TensorFlow</d> 
+      </tr>
+          <tr>
+    <td scope="row">BUIR</td>
+        <td>Lee et al. <a href="https://arxiv.org/abs/2105.06323" target="_blank">Bootstrapping User and Item Representations for One-Class Collaborative Filtering</a>, SIGIR'21.
+         </td> <td>Graph + DA</d> <td>PyTorch</d>
+      </tr>
+        <tr>
+    <td scope="row">SSL4Rec</td>
+        <td>Yao et al. <a href="https://dl.acm.org/doi/abs/10.1145/3459637.3481952" target="_blank">Self-supervised Learning for Large-scale Item Recommendations</a>, CIKM'21.
+	     </td> <td>Graph + CL</d>  <td>PyTorch</d>
+      </tr>
+    <tr>
+    <td scope="row">SelfCF</td>
+        <td>Zhou et al. <a href="https://arxiv.org/abs/2107.03019" target="_blank">SelfCF: A Simple Framework for Self-supervised Collaborative Filtering</a>, arXiv'21.
+         </td> <td>Graph + DA</d> <td>PyTorch</d>
+      </tr>
+    <tr>
+    <td scope="row">LightGCN</td>
+        <td>He et al. <a href="https://dl.acm.org/doi/10.1145/3397271.3401063" target="_blank">LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation</a>, SIGIR'20.
+	     </td> <td>Graph</d>  <td>PyTorch</d>
+      </tr>
+         <tr>
+    <td scope="row">MF</td>
+        <td>Yehuda et al. <a href="https://ieeexplore.ieee.org/abstract/document/5197422" target="_blank">Matrix Factorization Techniques for Recommender Systems</a>, IEEE Computer'09.
+	     </td> <td>Graph</d>  <td>PyTorch</d> 
+      </tr>
+  </table>  
+* CL is short for contrastive learning (including data augmentation); DA is short for data augmentation only
+
+<h2>Leaderboard</h2>
+The results are obtained on the dataset of <b>Yelp2018</b>. We performed grid search for the best hyperparameters. <br>
+General hyperparameter settings are: batch_size: 2048, emb_size: 64, learning rate: 0.001, L2 reg: 0.0001. <br><br>
+
+
+|  Model   |      Recall@20      | NDCG@20 | Hyperparameter settings                                                                             |
+|:--------:|:-------------------:|:-------:|:----------------------------------------------------------------------------------------------------|
+|   MF    |       0.0543        | 0.0445  |          |
+|   LightGCN    |       0.0639        | 0.0525  |     layer=3     |
+|   NCL    |       0.0670        | 0.0562  | layer=3, ssl_reg=1e-6, proto_reg=1e-7, tau=0.05, hyper_layers=1, alpha=1.5, num_clusters=2000 |
+|   SGL    |       0.0675        | 0.0555  |     λ=0.1, ρ=0.1, tau=0.2 layer=3     |
+|  MixGCF  |       0.0691        | 0.0577  |      layer=3, n_nes=64, layer=3       |
+| DirectAU |       0.0695        | 0.0583  |             𝛾=2, layer=3             |
+|  SimGCL  |       0.0721        | 0.0601  |   λ=0.5, eps=0.1, tau=0.2, layer=3    |
+| XSimGCL  |       0.0723        | 0.0604  | λ=0.2, eps=0.2, l∗=1 tau=0.15 layer=3 |
+
+<h2>Implement Your Model</h2>
+ 
+1. Create a **.conf** file for your model in the directory named conf.
+2. Make your model **inherit** the proper base class.
+3. **Reimplement** the following functions.
+	+ *build*(), *train*(), *save*(), *predict*()
+4. Register your model in **main.py**.
+
+
+
+<h2>Related Datasets</h2>
+<div>
+ <table class="table table-hover table-bordered">
+  <tr>
+    <th rowspan="2" scope="col">Data Set</th>
+    <th colspan="5" scope="col" class="text-center">Basic Meta</th>
+    <th colspan="3" scope="col" class="text-center">User Context</th> 
+    </tr>
+  <tr>
+    <th class="text-center">Users</th>
+    <th class="text-center">Items</th>
+    <th colspan="2" class="text-center">Ratings (Scale)</th>
+    <th class="text-center">Density</th>
+    <th class="text-center">Users</th>
+    <th colspan="2" class="text-center">Links (Type)</th>
+    </tr>   
+   <tr>
+    <td><a href="https://pan.baidu.com/s/1hrJP6rq" target="_blank"><b>Douban</b></a> </td>
+    <td>2,848</td>
+    <td>39,586</td>
+    <td width="6%">894,887</td>
+    <td width="10%">[1, 5]</td>
+    <td>0.794%</td>
+    <td width="4%">2,848</td>
+    <td width="5%">35,770</td>
+    <td>Trust</td>
+    </tr> 
+	 <tr>
+    <td><a href="http://files.grouplens.org/datasets/hetrec2011/hetrec2011-lastfm-2k.zip" target="_blank"><b>LastFM</b></a> </td>
+    <td>1,892</td>
+    <td>17,632</td>
+    <td width="6%">92,834</td>
+    <td width="10%">implicit</td>
+    <td>0.27%</td>
+    <td width="4%">1,892</td>
+    <td width="5%">25,434</td>
+    <td>Trust</td>
+    </tr> 
+    <tr>
+    <td><a href="https://www.dropbox.com/sh/h97ymblxt80txq5/AABfSLXcTu0Beib4r8P5I5sNa?dl=0" target="_blank"><b>Yelp</b></a> </td>
+    <td>19,539</td>
+    <td>21,266</td>
+    <td width="6%">450,884</td>
+    <td width="10%">implicit</td>
+    <td>0.11%</td>
+    <td width="4%">19,539</td>
+    <td width="5%">864,157</td>
+    <td>Trust</td>
+    </tr>
+    <tr>
+    <td><a href="https://www.dropbox.com/sh/20l0xdjuw0b3lo8/AABBZbRg9hHiN42EHqBSvLpta?dl=0" target="_blank"><b>Amazon-Book</b></a> </td>
+    <td>52,463</td>
+    <td>91,599</td>
+    <td width="6%">2,984,108</td>
+    <td width="10%">implicit</td>
+    <td>0.11%</td>
+    <td width="4%">-</td>
+    <td width="5%">-</td>
+    <td>-</td>
+    </tr>  
+  </table>
+</div>
+
+
+

SELFRec.py

@@ -0,0 +1,28 @@
+from data.loader import FileIO
+
+
+class SELFRec(object):
+    def __init__(self, config):
+        self.social_data = []
+        self.feature_data = []
+        self.config = config
+        if config['model.type'] == 'sequential':
+            self.training_data, self.test_data = FileIO.load_data_set(config['sequence.data'], config['model.type'])
+        else:
+            self.training_data = FileIO.load_data_set(config['training.set'], config['model.type'])
+            self.test_data = FileIO.load_data_set(config['test.set'], config['model.type'])
+
+        self.kwargs = {}
+        if config.contain('social.data'):
+            social_data = FileIO.load_social_data(self.config['social.data'])
+            self.kwargs['social.data'] = social_data
+        # if config.contains('feature.data'):
+        #     self.social_data = FileIO.loadFeature(config,self.config['feature.data'])
+        print('Reading data and preprocessing...')
+
+    def execute(self):
+        # import the model module
+        import_str = 'from model.'+ self.config['model.type'] +'.' + self.config['model.name'] + ' import ' + self.config['model.name']
+        exec(import_str)
+        recommender = self.config['model.name'] + '(self.config,self.training_data,self.test_data,**self.kwargs)'
+        eval(recommender).execute()

base/graph_recommender.py

@@ -0,0 +1,133 @@
+
+from base.recommender import Recommender
+from data.ui_graph import Interaction
+from util.algorithm import find_k_largest
+from time import strftime, localtime, time
+from data.loader import FileIO
+from os.path import abspath
+from util.evaluation import ranking_evaluation
+import sys
+from util.conf import OptionConf
+
+
+
+class GraphRecommender(Recommender):
+    def __init__(self, conf, training_set, test_set, **kwargs):
+        super(GraphRecommender, self).__init__(conf, training_set, test_set, **kwargs)
+        self.data = Interaction(conf, training_set, test_set)
+        self.bestPerformance = []
+        top = self.ranking['-topN'].split(',')
+        self.topN = [int(num) for num in top]
+        self.max_N = max(self.topN)
+
+    def print_model_info(self):
+        super(GraphRecommender, self).print_model_info()
+        # # print dataset statistics
+        print('Training Set Size: (user number: %d, item number %d, interaction number: %d)' % (self.data.training_size()))
+        print('Test Set Size: (user number: %d, item number %d, interaction number: %d)' % (self.data.test_size()))
+        print('=' * 80)
+
+    def build(self):
+        pass
+
+    def train(self):
+        pass
+
+    def predict(self, u):
+        pass
+
+    def test(self):
+        def process_bar(num, total):
+            rate = float(num) / total
+            ratenum = int(50 * rate)
+            r = '\rProgress: [{}{}]{}%'.format('+' * ratenum, ' ' * (50 - ratenum), ratenum*2)
+            sys.stdout.write(r)
+            sys.stdout.flush()
+
+        # predict
+        rec_list = {}
+        user_count = len(self.data.test_set)
+        for i, user in enumerate(self.data.test_set):
+            candidates = self.predict(user)
+            # predictedItems = denormalize(predictedItems, self.data.rScale[-1], self.data.rScale[0])
+            rated_list, li = self.data.user_rated(user)
+            for item in rated_list:
+                candidates[self.data.item[item]] = -10e8
+            ids, scores = find_k_largest(self.max_N, candidates)
+            item_names = [self.data.id2item[iid] for iid in ids]
+            rec_list[user] = list(zip(item_names, scores))
+            if i % 1000 == 0:
+                process_bar(i, user_count)
+        process_bar(user_count, user_count)
+        print('')
+        return rec_list
+
+    def evaluate(self, rec_list):
+        self.recOutput.append('userId: recommendations in (itemId, ranking score) pairs, * means the item is hit.\n')
+        for user in self.data.test_set:
+            line = user + ':'
+            for item in rec_list[user]:
+                line += ' (' + item[0] + ',' + str(item[1]) + ')'
+                if item[0] in self.data.test_set[user]:
+                    line += '*'
+            line += '\n'
+            self.recOutput.append(line)
+        current_time = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
+        # output prediction result
+        out_dir = self.output['-dir']
+        file_name = self.config['model.name'] + '@' + current_time + '-top-' + str(self.max_N) + 'items' + '.txt'
+        FileIO.write_file(out_dir, file_name, self.recOutput)
+        print('The result has been output to ', abspath(out_dir), '.')
+        file_name = self.config['model.name'] + '@' + current_time + '-performance' + '.txt'
+        args = OptionConf(self.config['SimGCL'])
+        self.result += ranking_evaluation(self.data.test_set, rec_list, self.topN)
+        self.model_log.add('###Evaluation Results###')
+        self.model_log.add(self.result)
+        FileIO.write_file(out_dir, file_name, self.result)
+        print('The result of %s:\n%s' % (self.model_name, ''.join(self.result)))
+
+    def fast_evaluation(self, epoch):
+        print('evaluating the model...')
+        rec_list = self.test()
+        measure = ranking_evaluation(self.data.test_set, rec_list, [self.max_N])
+        if len(self.bestPerformance) > 0:
+            count = 0
+            performance = {}
+            for m in measure[1:]:
+                k, v = m.strip().split(':')
+                performance[k] = float(v)
+            for k in self.bestPerformance[1]:
+                if self.bestPerformance[1][k] > performance[k]:
+                    count += 1
+                else:
+                    count -= 1
+            if count < 0:
+                self.bestPerformance[1] = performance
+                self.bestPerformance[0] = epoch + 1
+                self.save()
+        else:
+            self.bestPerformance.append(epoch + 1)
+            performance = {}
+            for m in measure[1:]:
+                k, v = m.strip().split(':')
+                performance[k] = float(v)
+                self.bestPerformance.append(performance)
+            self.save()
+        print('-' * 120)
+        print('Real-Time Ranking Performance ' + ' (Top-' + str(self.max_N) + ' Item Recommendation)')
+        measure = [m.strip() for m in measure[1:]]
+        print('*Current Performance*')
+        print('Epoch:', str(epoch + 1) + ',', ' | '.join(measure))
+        bp = ''
+        # for k in self.bestPerformance[1]:
+        #     bp+=k+':'+str(self.bestPerformance[1][k])+' | '
+        bp += 'Hit Ratio' + ':' + str(self.bestPerformance[1]['Hit Ratio']) + ' | '
+        bp += 'Precision' + ':' + str(self.bestPerformance[1]['Precision']) + ' | '
+        bp += 'Recall' + ':' + str(self.bestPerformance[1]['Recall']) + ' | '
+        # bp += 'F1' + ':' + str(self.bestPerformance[1]['F1']) + ' | '
+        bp += 'MDCG' + ':' + str(self.bestPerformance[1]['NDCG'])
+        print('*Best Performance* ')
+        print('Epoch:', str(self.bestPerformance[0]) + ',', bp)
+        print('-' * 120)
+        return measure
+

base/recommender.py

@@ -0,0 +1,77 @@
+from data.data import Data
+from util.conf import OptionConf
+from util.logger import Log
+from os.path import abspath
+from time import strftime, localtime, time
+
+
+class Recommender(object):
+    def __init__(self, conf, training_set, test_set, **kwargs):
+        self.config = conf
+        self.data = Data(self.config, training_set, test_set)
+        self.model_name = self.config['model.name']
+        self.ranking = OptionConf(self.config['item.ranking'])
+        self.emb_size = int(self.config['embbedding.size'])
+        self.maxEpoch = int(self.config['num.max.epoch'])
+        self.batch_size = int(self.config['batch_size'])
+        self.lRate = float(self.config['learnRate'])
+        self.reg = float(self.config['reg.lambda'])
+        self.output = OptionConf(self.config['output.setup'])
+        current_time = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
+        self.model_log = Log(self.model_name, self.model_name + ' ' + current_time)
+        self.result = []
+        self.recOutput = []
+
+    def initializing_log(self):
+        self.model_log.add('### model configuration ###')
+        for k in self.config.config:
+            self.model_log.add(k + '=' + self.config[k])
+
+    def print_model_info(self):
+        print('Model:', self.config['model.name'])
+        print('Training Set:', abspath(self.config['training.set']))
+        print('Test Set:', abspath(self.config['test.set']))
+        print('Embedding Dimension:', self.emb_size)
+        print('Maximum Epoch:', self.maxEpoch)
+        print('Learning Rate:', self.lRate)
+        print('Batch Size:', self.batch_size)
+        print('Regularization Parameter:',  self.reg)
+        parStr = ''
+        if self.config.contain(self.config['model.name']):
+            args = OptionConf(self.config[self.config['model.name']])
+            for key in args.keys():
+                parStr += key[1:] + ':' + args[key] + '  '
+            print('Specific parameters:', parStr)
+
+    def build(self):
+        pass
+
+    def train(self):
+        pass
+
+    def predict(self, u):
+        pass
+
+    def test(self):
+        pass
+
+    def save(self):
+        pass
+
+    def load(self):
+        pass
+
+    def evaluate(self, rec_list):
+        pass
+
+    def execute(self):
+        self.initializing_log()
+        self.print_model_info()
+        print('Initializing and building model...')
+        self.build()
+        print('Training Model...')
+        self.train()
+        print('Testing...')
+        rec_list = self.test()
+        print('Evaluating...')
+        self.evaluate(rec_list)

base/tf_interface.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+import numpy as np
+
+
+class TFGraphInterface(object):
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def convert_sparse_mat_to_tensor(adj):
+        row, col = adj.nonzero()
+        indices = np.array(list(zip(row, col)))
+        adj_tensor = tf.SparseTensor(indices=indices, values=adj.data, dense_shape=adj.shape)
+        return adj_tensor
+
+    @staticmethod
+    def convert_sparse_mat_to_tensor_inputs(X):
+        coo = X.tocoo()
+        indices = np.mat([coo.row, coo.col]).transpose()
+        return indices, coo.data, coo.shape

base/torch_interface.py

@@ -0,0 +1,12 @@
+import torch
+
+class TorchGraphInterface(object):
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def convert_sparse_mat_to_tensor(X):
+        coo = X.tocoo()
+        i = torch.LongTensor([coo.row, coo.col])
+        v = torch.from_numpy(coo.data).float()
+        return torch.sparse.FloatTensor(i, v, coo.shape)

conf/BUIR.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=BUIR
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=200
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+BUIR=-n_layer 2 -tau 0.995 -drop_rate 0.2
+output.setup=-dir ./results/

conf/DirectAU.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=DirectAU
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=50
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+DirectAU=-gamma 2 -n_layers 3
+output.setup=-dir ./results/

conf/FRGCF.conf

@@ -0,0 +1,17 @@
+
+training.set=./dataset/douban-book/train.txt
+test.set=./dataset/douban-book/test.txt
+model.name=FRGCF
+model.type=graph
+
+evaluation.setup=-topN 20,10
+item.ranking=-topN 20,10
+
+embbedding.size=64
+num.max.epoch=5
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+
+output.setup=-dir ./results/
+FRGCF=-n_layer 3 -temp 0.2 -lambda1 0.1 -lambda2 0.1 -lambda3 0.05 -mu 1.0 -cluster_num 64 -rating_threshold 4 -partition_mode douban_45_proxy -seed 2026

conf/LightGCN.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=LightGCN
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=500
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+LightGCN=-n_layer 2
+output.setup=-dir ./results/

conf/MF.conf

@@ -0,0 +1,11 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=MF
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=100
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+output.setup=-dir ./results/

conf/MHCN.conf

@@ -0,0 +1,13 @@
+training.set=./dataset/douban-book/train.txt
+test.set=./dataset/douban-book/test.txt
+social.data=./dataset/douban-book/trust.txt
+model.name=MHCN
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=30
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+MHCN=-n_layer 2 -ss_rate 0.01
+output.setup=-dir ./results/

conf/MixGCF.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=MixGCF
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=500
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+MixGCF=-n_layer 3 -n_negs 64
+output.setup=-dir ./results/

conf/NCL.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=NCL
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=120
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+NCL=-n_layer 3 -ssl_reg 1e-6 -proto_reg 1e-7 -tau 0.05 -hyper_layers 1 -alpha 1.5 -num_clusters 2000
+output.setup=-dir ./results/

conf/SEPT.conf

@@ -0,0 +1,13 @@
+training.set=./dataset/douban-book/train.txt
+test.set=./dataset/douban-book/test.txt
+social.data=./dataset/douban-book/trust.txt
+model.name=SEPT
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=30
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+SEPT=-n_layer 2 -ss_rate 0.005 -drop_rate 0.3 -ins_cnt 10
+output.setup=-dir ./results/

conf/SGL.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/iFashion/train.txt
+test.set=./dataset/iFashion/test.txt
+model.name=SGL
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=20
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+SGL=-n_layer 2 -lambda 0.1 -droprate 0.1 -augtype 1 -temp 0.2
+output.setup=-dir ./results/

conf/SSL4Rec.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/ml-1M/train.txt
+test.set=./dataset/ml-1M/test.txt
+model.name=SSL4Rec
+model.type=graph
+item.ranking=-topN 20
+embbedding.size=64
+num.max.epoch=100
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+SSL4Rec=-tau 0.07 -alpha 0.1 -drop 0.1
+output.setup=-dir ./results/

conf/SelfCF.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=SelfCF
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=100
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+SelfCF=-n_layer 2 -tau 0.05
+output.setup=-dir ./results/

conf/SimGCL.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/ml-1M/train.txt
+test.set=./dataset/ml-1M/test.txt
+model.name=SimGCL
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=120
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+SimGCL=-n_layer 3 -lambda 0.5 -eps 0.1 -cl_weight 0.01 -temp 0.2
+output.setup=-dir ./results/

conf/XSimGCL.conf

@@ -0,0 +1,12 @@
+training.set=./dataset/yelp2018/train.txt
+test.set=./dataset/yelp2018/test.txt
+model.name=XSimGCL
+model.type=graph
+item.ranking=-topN 10,20
+embbedding.size=64
+num.max.epoch=20
+batch_size=2048
+learnRate=0.001
+reg.lambda=0.0001
+XSimGCL=-n_layer 2 -l* 1 -lambda 0.2 -eps 0.2 -tau 0.15
+output.setup=-dir ./results/