import numpy as np
import pandas as pd
from .explainer import Explainer
from tqdm.auto import tqdm
[docs]
class Exp_MTER(Explainer):
"""Explainer for MTER (Multi-task Explainable Recommendation).
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_MTER'
References
----------
[1] Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. https://arxiv.org/pdf/1806.03568.pdf
[2] https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb
"""
def __init__(self, rec_model, dataset, name="Exp_MTER"):
super().__init__(name, rec_model, dataset)
# self.G1 = self.model.G1
# self.G2 = self.model.G2
# self.G3 = self.model.G3
# self.U = self.model.U
# self.I = self.model.I
# self.A = self.model.A
# self.O = self.model.O
if self.model is None:
raise NotImplementedError("The model is None.")
[docs]
def explain_one_recommendation_to_user(self, user_id, item_id, **kwargs):
"""Get aspect performs best and user's opinion word of that aspect.
Parameters
----------
user_id: str
One user's id.
item_id: str
One item's id.
feature_k: int, optional, default:3
Number of features in explanations created by explainer.
num_top_opinions: int, optional, default:3
Number of opinions used to explain each aspect.
Returns
-------
explanation: dict
Explanations as {aspect: [{opinion: score}, {opinion: score}, ...}], aspect: [{opinion: score}, {opinion: score}, ...]}
"""
# feature_k=3, num_top_opinions=3
self.num_top_aspects = kwargs.get("feature_k", 3)
self.num_top_opinions = kwargs.get("num_top_opinions", 3)
user_id = self.dataset.uid_map[user_id]
item_id = self.dataset.iid_map[item_id]
id_aspect_map = {v: k for k, v in self.dataset.sentiment.aspect_id_map.items()}
id_opinion_map = {
v: k for k, v in self.dataset.sentiment.opinion_id_map.items()
}
item_id_list = np.array(
[k for k in self.dataset.sentiment.item_sentiment.keys()]
)
idx_list = np.array([k for k in self.dataset.sentiment.sentiment.keys()])
if item_id not in item_id_list:
idx_selected = np.random.choice(idx_list, 3, replace=False)
item_aspect_ids = np.array(
list(
set(
[
tup[0]
for idx in idx_selected
for tup in self.dataset.sentiment.sentiment[idx]
]
)
)
)
item_opinion_ids = np.array(
list(
set(
[
tup[1]
for idx in idx_selected
for tup in self.dataset.sentiment.sentiment[idx]
]
)
)
)
else:
item_aspect_ids = np.array(
list(
set(
[
tup[0]
for idx in self.dataset.sentiment.item_sentiment[
item_id
].values()
for tup in self.dataset.sentiment.sentiment[idx]
]
)
)
)
item_opinion_ids = np.array(
list(
set(
[
tup[1]
for idx in self.dataset.sentiment.item_sentiment[
item_id
].values()
for tup in self.dataset.sentiment.sentiment[idx]
]
)
)
)
# item_aspects = [id_aspect_map[idx] for idx in item_aspect_ids]
ts1 = np.einsum("abc, a->bc", self.model.G1, self.model.U[user_id])
ts2 = np.einsum("bc, b->c", ts1, self.model.I[item_id])
predicted_aspect_scores = np.einsum("c, Mc->M", ts2, self.model.A)
top_aspect_ids = item_aspect_ids[
(predicted_aspect_scores[item_aspect_ids]).argsort()[: self.num_top_aspects]
]
top_aspects = [id_aspect_map[idx] for idx in top_aspect_ids]
explanations = {}
for top_aspect_id, top_aspect in zip(top_aspect_ids, top_aspects):
ts1_G2 = np.einsum("abc, a -> bc", self.model.G2, self.model.U[user_id])
ts2_G2 = np.einsum("bc, b -> c", ts1_G2, self.model.A[top_aspect_id])
predicted_user_aspect_opinion_scores = np.einsum(
"c, Mc -> M", ts2_G2, self.model.O
)
ts1_G3 = np.einsum("abc, a -> bc", self.model.G3, self.model.I[item_id])
ts2_G3 = np.einsum("bc, b -> c", ts1_G3, self.model.A[top_aspect_id])
predicted_item_aspect_opinion_scores = np.einsum(
"c, Mc -> M", ts2_G3, self.model.O
)
predicted_aspect_opinion_scores = np.multiply(
predicted_user_aspect_opinion_scores,
predicted_item_aspect_opinion_scores,
)
top_opinion_ids = item_opinion_ids[
(-predicted_aspect_opinion_scores[item_opinion_ids]).argsort()[
: self.num_top_opinions
]
]
top_opinions_scores = {}
for top_opinion_id in top_opinion_ids:
top_opinions_scores[id_opinion_map[top_opinion_id]] = (
predicted_aspect_opinion_scores[top_opinion_id]
)
explanations[top_aspect] = top_opinions_scores
return explanations