import pandas as pd
import numpy as np
from .explainer import Explainer
[docs]
class Exp_EMF(Explainer):
"""Explainer from Explainable Matrix Factorization for Collaborative Filtering. Explains by W matrix in the paper.
Parameters
----------
rec_model: object, recommender model
The recommender model to be explained.
dataset: object, dataset
The dataset object that is used to explain.
name: string, optional, default: 'Exp_EMF'
References
----------
[1] B. Abdollahi and O. Nasraoui, “Explainable Matrix Factorization for Collaborative Filtering,” \
ACM Press, 2016, pp. 5-6. doi: 10.1145/2872518.2889405.
"""
def __init__(self, rec_model, dataset, name="Exp_EMF"):
super().__init__(name=name, rec_model=rec_model, dataset=dataset)
[docs]
def explain_one_recommendation_to_user(self, user_id, item_id, **kwargs):
"""Provide explanation for one user and one item
Parameters
----------
user_id: str
One user's id.
item_id: str
One item's id.
Returns
-------
explanations: float
The W matrix value of the user and item.
"""
if self.model is None:
raise NotImplementedError("The model is None.")
if not hasattr(self.model, "edge_weight_matrix"):
raise AttributeError("The explainer does not support this recommender.")
if self.model.edge_weight_matrix is None:
raise NotImplementedError("The model is not trained yet.")
uir_df = pd.DataFrame(
np.array(self.dataset.uir_tuple).T, columns=["user", "item", "rating"]
)
uir_df["user"] = uir_df["user"].astype(int)
uir_df["item"] = uir_df["item"].astype(int)
if user_id not in self.dataset.uid_map:
return 0
if item_id not in self.dataset.iid_map:
return 0
user_idx = self.dataset.uid_map[user_id]
item_idx = self.dataset.iid_map[item_id]
if self.model.is_unknown_user(user_idx):
return 0
if self.model.is_unknown_item(item_idx):
return 0
W = self.model.edge_weight_matrix
explanation = W[user_idx, item_idx]
return explanation