Source code for recbole.model.general_recommender.lightgcn

# -*- coding: utf-8 -*-
# @Time   : 2020/8/31
# @Author : Changxin Tian
# @Email  : cx.tian@outlook.com

# UPDATE:
# @Time   : 2020/9/16, 2021/12/22
# @Author : Shanlei Mu, Gaowei Zhang
# @Email  : slmu@ruc.edu.cn, 1462034631@qq.com

r"""
LightGCN
################################################

Reference:
    Xiangnan He et al. "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation." in SIGIR 2020.

Reference code:
    https://github.com/kuandeng/LightGCN
"""

import numpy as np
import scipy.sparse as sp
import torch

from recbole.model.abstract_recommender import GeneralRecommender
from recbole.model.init import xavier_uniform_initialization
from recbole.model.loss import BPRLoss, EmbLoss
from recbole.utils import InputType


[docs]class LightGCN(GeneralRecommender): r"""LightGCN is a GCN-based recommender model. LightGCN includes only the most essential component in GCN — neighborhood aggregation — for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(LightGCN, self).__init__(config, dataset) # load dataset info self.interaction_matrix = dataset.inter_matrix(form='coo').astype(np.float32) # load parameters info self.latent_dim = config['embedding_size'] # int type:the embedding size of lightGCN self.n_layers = config['n_layers'] # int type:the layer num of lightGCN self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization self.require_pow = config['require_pow'] # define layers and loss self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.latent_dim) self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.latent_dim) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # generate intermediate data self.norm_adj_matrix = self.get_norm_adj_mat().to(self.device) # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e']
[docs] def get_norm_adj_mat(self): r"""Get the normalized interaction matrix of users and items. Construct the square matrix from the training data and normalize it using the laplace matrix. .. math:: A_{hat} = D^{-0.5} \times A \times D^{-0.5} Returns: Sparse tensor of the normalized interaction matrix. """ # build adj matrix A = sp.dok_matrix((self.n_users + self.n_items, self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) # add epsilon to avoid divide by zero Warning diag = np.array(sumArr.flatten())[0] + 1e-7 diag = np.power(diag, -0.5) D = sp.diags(diag) L = D * A * D # covert norm_adj matrix to tensor L = sp.coo_matrix(L) row = L.row col = L.col i = torch.LongTensor([row, col]) data = torch.FloatTensor(L.data) SparseL = torch.sparse.FloatTensor(i, data, torch.Size(L.shape)) return SparseL
[docs] def get_ego_embeddings(self): r"""Get the embedding of users and items and combine to an embedding matrix. Returns: Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim] """ user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0) return ego_embeddings
[docs] def forward(self): all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for layer_idx in range(self.n_layers): all_embeddings = torch.sparse.mm(self.norm_adj_matrix, all_embeddings) embeddings_list.append(all_embeddings) lightgcn_all_embeddings = torch.stack(embeddings_list, dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings
[docs] def calculate_loss(self, interaction): # clear the storage variable when training 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 user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate BPR Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings, require_pow=self.require_pow) loss = mf_loss + self.reg_weight * reg_loss return loss
[docs] def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores
[docs] def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1)