update (#1352)

Author: rolf-moz

merino/curated_recommendations/ml_backends/static_local_model.py

@@ -303,16 +303,20 @@ def _get_section(section_name: str, thresholds: list[float]) -> InterestVectorCo
         )
 
     def _build_local(
-        self, model_id, surface_id, small_experiment=False
+        self, model_id, surface_id, small_experiment=False, include_local_reranking=False
     ) -> InferredLocalModel | None:
         model_thresholds = THRESHOLDS_V3_NORMALIZED
         private_features: list[str] | None = None
 
-        section_features = {
-            a: self._get_section(a, model_thresholds)
-            for a in BASE_SECTIONS_FOR_LOCAL_MODEL
-            if a not in self.limited_topics_set
-        }
+        section_features = (
+            {
+                a: self._get_section(a, model_thresholds)
+                for a in BASE_SECTIONS_FOR_LOCAL_MODEL
+                if a not in self.limited_topics_set
+            }
+            if include_local_reranking
+            else {}
+        )
 
         private_features = self.v3_limited_topics + [TIME_ZONE_OFFSET_INFERRED_KEY]
 

merino/curated_recommendations/rankers/contextual_ranker.py

@@ -34,7 +34,7 @@
 # we're looking at are total impressions and we care about impressions with the inferred interests. When
 # contexual is rolled out we can use a dynamic computed value based on daily impressions.
 
-CONTEXUAL_AVG_BETA_VALUE = 4000
+CONTEXUAL_AVG_BETA_VALUE = 12000
 
 logger = logging.getLogger(__name__)