/
ild.py
53 lines (43 loc) · 1.49 KB
/
ild.py
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import numpy as np
from collections import defaultdict
from .base import Metric
from typing import Any
np.seterr(all="raise")
class ILD(Metric):
"""Intra-List Diversity.
This is used to measure the diversity of an individual user’s recommendations and quantifies user-novelty.
"""
def __init__(self, items_distance, *args, **kwargs):
"""__init__.
Args:
args:
kwargs:
items_distance:
"""
super().__init__(*args, **kwargs)
self.items_distance = items_distance
self.users_items_recommended = defaultdict(list)
self.users_local_ild = defaultdict(float)
def compute(self, uid: int):
"""compute.
Args:
uid (int): user id
"""
user_num_items_recommended = len(self.users_items_recommended[uid])
if user_num_items_recommended == 0 or user_num_items_recommended == 1:
return 1.0
else:
return self.users_local_ild[uid] / (
user_num_items_recommended * (user_num_items_recommended - 1) / 2
)
def update_recommendation(self, uid: int, item: int, reward: float):
"""update_recommendation.
Args:
uid (int): user id
item (int): item id
reward (float): reward
"""
self.users_local_ild[uid] += np.sum(
self.items_distance[self.users_items_recommended[uid], item]
)
self.users_items_recommended[uid].append(item)