News Locality Calibration

src/poprox_recommender/components/diversifiers/__init__.py

@@ -1,5 +1,7 @@
+from poprox_recommender.components.diversifiers.calibration import Calibrator
+from poprox_recommender.components.diversifiers.locality_calibration import LocalityCalibrator
 from poprox_recommender.components.diversifiers.mmr import MMRDiversifier
 from poprox_recommender.components.diversifiers.pfar import PFARDiversifier
 from poprox_recommender.components.diversifiers.topic_calibration import TopicCalibrator
 
-__all__ = ["MMRDiversifier", "PFARDiversifier", "TopicCalibrator"]
+__all__ = ["MMRDiversifier", "PFARDiversifier", "Calibrator", "TopicCalibrator", "LocalityCalibrator"]

src/poprox_recommender/components/diversifiers/calibration.py

@@ -0,0 +1,77 @@
+from collections import defaultdict
+
+import numpy as np
+
+from poprox_concepts import Article
+from poprox_recommender.lkpipeline import Component
+from poprox_recommender.topics import normalized_category_count
+
+
+# General calibration uses MMR
+# to rerank recommendations according to
+# certain calibration context (e.g. news topic, locality)
+class Calibrator(Component):
+    def __init__(self, theta: float = 0.1, num_slots=10):
+        # Theta term controls the score and calibration tradeoff, the higher
+        # the theta the higher the resulting recommendation will be calibrated.
+        self.theta = theta
+        self.num_slots = num_slots
+
+    def __call__():
+        pass
+
+    def add_article_to_categories(self, rec_categories_with_candidate, article):
+        pass
+
+    def normalized_categories_with_candidate(self, rec_categories, article):
+        rec_categories_with_candidate = rec_categories.copy()
+        self.add_article_to_categories(rec_categories_with_candidate, article)
+        return normalized_category_count(rec_categories_with_candidate)
+
+    def calibration(self, relevance_scores, articles, preferences, theta, topk) -> list[Article]:
+        # MR_i = \theta * reward_i - (1 - \theta)*C(S + i) # C is calibration
+        # R is all candidates (not selected yet)
+
+        recommendations = []  # final recommendation (topk index)
+        rec_categories = defaultdict(int)  # frequency distribution of categories of S
+
+        for _ in range(topk):
+            candidate = None  # next item
+            best_candidate_score = float("-inf")
+
+            for article_idx, article_score in enumerate(relevance_scores):  # iterate R for next item
+                if article_idx in recommendations:
+                    continue
+
+                normalized_candidate_topics = self.normalized_categories_with_candidate(
+                    rec_categories, articles[article_idx]
+                )
+                calibration = compute_kl_divergence(preferences, normalized_candidate_topics)
+
+                adjusted_candidate_score = (1 - theta) * article_score - (theta * calibration)
+                if adjusted_candidate_score > best_candidate_score:
+                    best_candidate_score = adjusted_candidate_score
+                    candidate = article_idx
+
+            if candidate is not None:
+                recommendations.append(candidate)
+                self.add_article_to_categories(rec_categories, articles[candidate])
+
+        return recommendations
+
+
+# from https://github.com/CCRI-POPROX/poprox-recommender/blob/feature/experiment0/tests/test_calibration.ipynb
+def compute_kl_divergence(interacted_distr, reco_distr, kl_div=0.0, alpha=0.01):
+    """
+    KL (p || q), the lower the better.
+
+    alpha is not really a tuning parameter, it's just there to make the
+    computation more numerically stable.
+    """
+    for category, score in interacted_distr.items():
+        reco_score = reco_distr.get(category, 0.0)
+        reco_score = (1 - alpha) * reco_score + alpha * score
+        if reco_score != 0.0:
+            kl_div += score * np.log2(score / reco_score)
+
+    return kl_div

src/poprox_recommender/components/diversifiers/locality_calibration.py

@@ -0,0 +1,37 @@
+import torch as th
+
+from poprox_concepts import ArticleSet, InterestProfile
+from poprox_recommender.components.diversifiers.calibration import Calibrator
+from poprox_recommender.topics import extract_locality, normalized_category_count
+
+
+# Locality Calibration uses MMR
+# to rerank recommendations according to
+# locality calibration
+class LocalityCalibrator(Calibrator):
+    def __init__(self, theta: float = 0.1, num_slots=10):
+        super().__init__(theta, num_slots)
+
+    def __call__(self, candidate_articles: ArticleSet, interest_profile: InterestProfile) -> ArticleSet:
+        normalized_locality_prefs = normalized_category_count(interest_profile.click_locality_counts)
+
+        if candidate_articles.scores is not None:
+            article_scores = th.sigmoid(th.tensor(candidate_articles.scores))
+        else:
+            article_scores = th.zeros(len(candidate_articles.articles))
+
+        article_scores = article_scores.cpu().detach().numpy()
+
+        article_indices = self.calibration(
+            article_scores,
+            candidate_articles.articles,
+            normalized_locality_prefs,
+            self.theta,
+            topk=self.num_slots,
+        )
+        return ArticleSet(articles=[candidate_articles.articles[int(idx)] for idx in article_indices])
+
+    def add_article_to_categories(self, rec_categories, article):
+        locality_list = extract_locality(article)
+        for locality in locality_list:
+            rec_categories[locality] = rec_categories.get(locality, 0) + 1

src/poprox_recommender/components/diversifiers/pfar.py

@@ -5,7 +5,7 @@
 from poprox_concepts import Article, ArticleSet, InterestProfile
 from poprox_recommender.lkpipeline import Component
 from poprox_recommender.pytorch.decorators import torch_inference
-from poprox_recommender.topics import GENERAL_TOPICS, extract_general_topics, normalized_topic_count
+from poprox_recommender.topics import GENERAL_TOPICS, extract_general_topics, normalized_category_count
 
 
 class PFARDiversifier(Component):
@@ -30,7 +30,7 @@ def __call__(self, candidate_articles: ArticleSet, interest_profile: InterestPro
             for topic, click_count in interest_profile.click_topic_counts.items():
                 topic_preferences[topic] = click_count
 
-        normalized_topic_prefs = normalized_topic_count(topic_preferences)
+        normalized_topic_prefs = normalized_category_count(topic_preferences)
 
         article_indices = pfar_diversification(
             article_scores,

src/poprox_recommender/components/diversifiers/topic_calibration.py

@@ -1,23 +1,16 @@
 from collections import defaultdict
 
-import numpy as np
 import torch as th
 
-from poprox_concepts import Article, ArticleSet, InterestProfile
-from poprox_recommender.lkpipeline import Component
-from poprox_recommender.topics import extract_general_topics, normalized_topic_count
+from poprox_concepts import ArticleSet, InterestProfile
+from poprox_recommender.components.diversifiers.calibration import Calibrator
+from poprox_recommender.topics import extract_general_topics, normalized_category_count
 
 
 # Topic Calibration uses MMR
 # to rerank recommendations according to
 # topic calibration
-class TopicCalibrator(Component):
-    def __init__(self, theta: float = 0.1, num_slots=10):
-        # Theta term controls the score and calibration tradeoff, the higher
-        # the theta the higher the resulting recommendation will be calibrated.
-        self.theta = theta
-        self.num_slots = num_slots
-
+class TopicCalibrator(Calibrator):
     def __call__(self, candidate_articles: ArticleSet, interest_profile: InterestProfile) -> ArticleSet:
         normalized_topic_prefs = self.compute_topic_dist(interest_profile)
 
@@ -28,7 +21,7 @@ def __call__(self, candidate_articles: ArticleSet, interest_profile: InterestPro
 
         article_scores = article_scores.cpu().detach().numpy()
 
-        article_indices = topic_calibration(
+        article_indices = self.calibration(
             article_scores,
             candidate_articles.articles,
             normalized_topic_prefs,
@@ -48,66 +41,10 @@ def compute_topic_dist(self, interest_profile):
             for topic, click_count in interest_profile.click_topic_counts.items():
                 topic_preferences[topic] += click_count
 
-        normalized_topic_prefs = normalized_topic_count(topic_preferences)
+        normalized_topic_prefs = normalized_category_count(topic_preferences)
         return normalized_topic_prefs
 
-
-def topic_calibration(relevance_scores, articles, topic_preferences, theta, topk) -> list[Article]:
-    # MR_i = \theta * reward_i - (1 - \theta)*C(S + i) # C is calibration
-    # R is all candidates (not selected yet)
-
-    recommendations = []  # final recommendation (topk index)
-    rec_topics = defaultdict(int)  # frequency distribution of topics of S
-
-    for k in range(topk):
-        candidate = None  # next item
-        best_candidate_score = float("-inf")
-
-        for article_idx, article_score in enumerate(relevance_scores):  # iterate R for next item
-            if article_idx in recommendations:
-                continue
-
-            normalized_candidate_topics = normalized_topics_with_candidate(rec_topics, articles[article_idx])
-            calibration = compute_kl_divergence(topic_preferences, normalized_candidate_topics)
-
-            adjusted_candidate_score = (1 - theta) * article_score - (theta * calibration)
-            if adjusted_candidate_score > best_candidate_score:
-                best_candidate_score = adjusted_candidate_score
-                candidate = article_idx
-
-        if candidate is not None:
-            recommendations.append(candidate)
-            add_article_to_topics(rec_topics, articles[candidate])
-
-    return recommendations
-
-
-def add_article_to_topics(rec_topics, article):
-    topics = extract_general_topics(article)
-    for topic in topics:
-        rec_topics[topic] = rec_topics.get(topic, 0) + 1
-
-
-def normalized_topics_with_candidate(rec_topics, article):
-    rec_topics_with_candidate = rec_topics.copy()
-    add_article_to_topics(rec_topics_with_candidate, article)
-    return normalized_topic_count(rec_topics_with_candidate)
-
-
-# from https://github.com/CCRI-POPROX/poprox-recommender/blob/feature/experiment0/tests/test_calibration.ipynb
-def compute_kl_divergence(interacted_distr, reco_distr):
-    """
-    KL (p || q), the lower the better.
-
-    alpha is not really a tuning parameter, it's just there to make the
-    computation more numerically stable.
-    """
-    kl_div = 0.0
-    alpha = 0.01
-    for genre, score in interacted_distr.items():
-        reco_score = reco_distr.get(genre, 0.0)
-        reco_score = (1 - alpha) * reco_score + alpha * score
-        if reco_score != 0.0:
-            kl_div += score * np.log2(score / reco_score)
-
-    return kl_div
+    def add_article_to_categories(self, rec_topics, article):
+        topics = extract_general_topics(article)
+        for topic in topics:
+            rec_topics[topic] = rec_topics.get(topic, 0) + 1

src/poprox_recommender/handler.py

@@ -4,7 +4,7 @@
 from poprox_concepts import ArticleSet
 from poprox_concepts.api.recommendations import RecommendationRequest, RecommendationResponse
 from poprox_recommender.recommenders import select_articles
-from poprox_recommender.topics import user_topic_preference
+from poprox_recommender.topics import user_locality_preference, user_topic_preference
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -49,6 +49,7 @@ def generate_recs(event, context):
     clicked_articles = ArticleSet(articles=clicked_articles)
 
     profile.click_topic_counts = user_topic_preference(req.past_articles, profile.click_history)
+    profile.click_locality_counts = user_locality_preference(req.past_articles, profile.click_history)
 
     outputs = select_articles(
         candidate_articles,

src/poprox_recommender/recommenders.py

@@ -3,7 +3,12 @@
 from typing import Any
 
 from poprox_concepts import ArticleSet, InterestProfile
-from poprox_recommender.components.diversifiers import MMRDiversifier, PFARDiversifier, TopicCalibrator
+from poprox_recommender.components.diversifiers import (
+    LocalityCalibrator,
+    MMRDiversifier,
+    PFARDiversifier,
+    TopicCalibrator,
+)
 from poprox_recommender.components.embedders import NRMSArticleEmbedder, NRMSUserEmbedder
 from poprox_recommender.components.filters import TopicFilter
 from poprox_recommender.components.joiners import Fill
@@ -85,7 +90,8 @@ def build_pipelines(num_slots: int, device: str) -> dict[str, Pipeline]:
     topk_ranker = TopkRanker(num_slots=num_slots)
     mmr = MMRDiversifier(num_slots=num_slots)
     pfar = PFARDiversifier(num_slots=num_slots)
-    calibrator = TopicCalibrator(num_slots=num_slots)
+    locality_calibrator = LocalityCalibrator(num_slots=num_slots)
+    topic_calibrator = TopicCalibrator(num_slots=num_slots)
     sampler = SoftmaxSampler(num_slots=num_slots, temperature=30.0)
 
     nrms_pipe = build_pipeline(
@@ -112,11 +118,19 @@ def build_pipelines(num_slots: int, device: str) -> dict[str, Pipeline]:
         num_slots=num_slots,
     )
 
-    cali_pipe = build_pipeline(
-        "NRMS+Calibration",
+    topic_cali_pipe = build_pipeline(
+        "NRMS+Topic+Calibration",
         article_embedder=article_embedder,
         user_embedder=user_embedder,
-        ranker=calibrator,
+        ranker=topic_calibrator,
+        num_slots=num_slots,
+    )
+
+    locality_cali_pipe = build_pipeline(
+        "NRMS+Locality+Calibration",
+        article_embedder=article_embedder,
+        user_embedder=user_embedder,
+        ranker=locality_calibrator,
         num_slots=num_slots,
     )
 
@@ -132,7 +146,8 @@ def build_pipelines(num_slots: int, device: str) -> dict[str, Pipeline]:
         "nrms": nrms_pipe,
         "mmr": mmr_pipe,
         "pfar": pfar_pipe,
-        "topic-cali": cali_pipe,
+        "topic-cali": topic_cali_pipe,
+        "locality-cali": locality_cali_pipe,
         "softmax": softmax_pipe,
     }