Source code for recbole.model.general_recommender.bpr

# -*- coding: utf-8 -*-
# @Time   : 2020/6/25
# @Author : Shanlei Mu
# @Email  : slmu@ruc.edu.cn

# UPDATE:
# @Time   : 2020/9/16
# @Author : Shanlei Mu
# @Email  : slmu@ruc.edu.cn

r"""
BPR
################################################
Reference:
    Steffen Rendle et al. "BPR: Bayesian Personalized Ranking from Implicit Feedback." in UAI 2009.
"""

import torch
import torch.nn as nn

from recbole.model.abstract_recommender import GeneralRecommender
from recbole.model.init import xavier_normal_initialization
from recbole.model.loss import BPRLoss
from recbole.utils import InputType


[docs]class BPR(GeneralRecommender): r"""BPR is a basic matrix factorization model that be trained in the pairwise way.""" input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(BPR, self).__init__(config, dataset) # load parameters info self.embedding_size = config["embedding_size"] # define layers and loss self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) self.loss = BPRLoss() # parameters initialization self.apply(xavier_normal_initialization)
[docs] def get_user_embedding(self, user): r"""Get a batch of user embedding tensor according to input user's id. Args: user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] Returns: torch.FloatTensor: The embedding tensor of a batch of user, shape: [batch_size, embedding_size] """ return self.user_embedding(user)
[docs] def get_item_embedding(self, item): r"""Get a batch of item embedding tensor according to input item's id. Args: item (torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ] Returns: torch.FloatTensor: The embedding tensor of a batch of item, shape: [batch_size, embedding_size] """ return self.item_embedding(item)
[docs] def forward(self, user, item): user_e = self.get_user_embedding(user) item_e = self.get_item_embedding(item) return user_e, item_e
[docs] def calculate_loss(self, interaction): user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_e, pos_e = self.forward(user, pos_item) neg_e = self.get_item_embedding(neg_item) pos_item_score, neg_item_score = torch.mul(user_e, pos_e).sum(dim=1), torch.mul( user_e, neg_e ).sum(dim=1) loss = self.loss(pos_item_score, neg_item_score) return loss
[docs] def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_e, item_e = self.forward(user, item) return torch.mul(user_e, item_e).sum(dim=1)
[docs] def full_sort_predict(self, interaction): user = interaction[self.USER_ID] user_e = self.get_user_embedding(user) all_item_e = self.item_embedding.weight score = torch.matmul(user_e, all_item_e.transpose(0, 1)) return score.view(-1)