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Merge pull request #74 from downeykking/main
FEA: add DirectAU and fix some bugs
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Original file line number | Diff line number | Diff line change |
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# r""" | ||
# DiretAU | ||
# ################################################ | ||
# Reference: | ||
# Chenyang Wang et al. "Towards Representation Alignment and Uniformity in Collaborative Filtering." in KDD 2022. | ||
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# Reference code: | ||
# https://github.com/THUwangcy/DirectAU | ||
# """ | ||
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import numpy as np | ||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
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from recbole.model.init import xavier_normal_initialization | ||
from recbole.utils import InputType | ||
from recbole.model.general_recommender import BPR | ||
from recbole_gnn.model.general_recommender import LightGCN | ||
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from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender | ||
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class DirectAU(GeneralGraphRecommender): | ||
input_type = InputType.PAIRWISE | ||
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def __init__(self, config, dataset): | ||
super(DirectAU, self).__init__(config, dataset) | ||
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# load parameters info | ||
self.embedding_size = config['embedding_size'] | ||
self.gamma = config['gamma'] | ||
self.encoder_name = config['encoder'] | ||
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# define encoder | ||
if self.encoder_name == 'MF': | ||
self.encoder = MFEncoder(config, dataset) | ||
elif self.encoder_name == 'LightGCN': | ||
self.encoder = LGCNEncoder(config, dataset) | ||
else: | ||
raise ValueError('Non-implemented Encoder.') | ||
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# storage variables for full sort evaluation acceleration | ||
self.restore_user_e = None | ||
self.restore_item_e = None | ||
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# parameters initialization | ||
self.apply(xavier_normal_initialization) | ||
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def forward(self, user, item): | ||
user_e, item_e = self.encoder(user, item) | ||
return F.normalize(user_e, dim=-1), F.normalize(item_e, dim=-1) | ||
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@staticmethod | ||
def alignment(x, y, alpha=2): | ||
return (x - y).norm(p=2, dim=1).pow(alpha).mean() | ||
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@staticmethod | ||
def uniformity(x, t=2): | ||
return torch.pdist(x, p=2).pow(2).mul(-t).exp().mean().log() | ||
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def calculate_loss(self, interaction): | ||
if self.restore_user_e is not None or self.restore_item_e is not None: | ||
self.restore_user_e, self.restore_item_e = None, None | ||
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user = interaction[self.USER_ID] | ||
item = interaction[self.ITEM_ID] | ||
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user_e, item_e = self.forward(user, item) | ||
align = self.alignment(user_e, item_e) | ||
uniform = self.gamma * (self.uniformity(user_e) + self.uniformity(item_e)) / 2 | ||
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return align, uniform | ||
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def predict(self, interaction): | ||
user = interaction[self.USER_ID] | ||
item = interaction[self.ITEM_ID] | ||
user_e = self.user_embedding(user) | ||
item_e = self.item_embedding(item) | ||
return torch.mul(user_e, item_e).sum(dim=1) | ||
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def full_sort_predict(self, interaction): | ||
user = interaction[self.USER_ID] | ||
if self.encoder_name == 'LightGCN': | ||
if self.restore_user_e is None or self.restore_item_e is None: | ||
self.restore_user_e, self.restore_item_e = self.encoder.get_all_embeddings() | ||
user_e = self.restore_user_e[user] | ||
all_item_e = self.restore_item_e | ||
else: | ||
user_e = self.encoder.user_embedding(user) | ||
all_item_e = self.encoder.item_embedding.weight | ||
score = torch.matmul(user_e, all_item_e.transpose(0, 1)) | ||
return score.view(-1) | ||
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class MFEncoder(BPR): | ||
def __init__(self, config, dataset): | ||
super(MFEncoder, self).__init__(config, dataset) | ||
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def forward(self, user_id, item_id): | ||
return super().forward(user_id, item_id) | ||
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def get_all_embeddings(self): | ||
user_embeddings = self.user_embedding.weight | ||
item_embeddings = self.item_embedding.weight | ||
return user_embeddings, item_embeddings | ||
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class LGCNEncoder(LightGCN): | ||
def __init__(self, config, dataset): | ||
super(LGCNEncoder, self).__init__(config, dataset) | ||
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def forward(self, user_id, item_id): | ||
user_all_embeddings, item_all_embeddings = self.get_all_embeddings() | ||
u_embed = user_all_embeddings[user_id] | ||
i_embed = item_all_embeddings[item_id] | ||
return u_embed, i_embed | ||
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def get_all_embeddings(self): | ||
return super().forward() |
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Original file line number | Diff line number | Diff line change |
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embedding_size: 64 | ||
encoder: "MF" # "MF" or "lightGCN" | ||
gamma: 0.5 | ||
weight_decay: 1e-6 | ||
train_batch_size: 256 | ||
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# n_layers: 3 # needed for LightGCN |
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