From f12c6fd57e5ce4f5077c0356954e530a69346cc5 Mon Sep 17 00:00:00 2001
From: Viktor Lofgren <vlofgren@marginalia.nu>
Date: Thu, 20 Apr 2023 16:00:59 +0200
Subject: [PATCH] Add a ranking parameter for biasing toward recent or old
 content.

---
 .../results/ResultRankingParameters.java      |  9 ++++++++
 .../nu/marginalia/ranking/ResultValuator.java | 21 ++++++++++++++-----
 2 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/code/api/index-api/src/main/java/nu/marginalia/index/client/model/results/ResultRankingParameters.java b/code/api/index-api/src/main/java/nu/marginalia/index/client/model/results/ResultRankingParameters.java
index b76d11d8..ff28c5d5 100644
--- a/code/api/index-api/src/main/java/nu/marginalia/index/client/model/results/ResultRankingParameters.java
+++ b/code/api/index-api/src/main/java/nu/marginalia/index/client/model/results/ResultRankingParameters.java
@@ -33,6 +33,9 @@ public class ResultRankingParameters {
     public double bm25PrioWeight;
     public double tcfWeight;
 
+    public TemporalBias temporalBias;
+    public double temporalBiasWeight;
+
     public static ResultRankingParameters sensibleDefaults() {
         return builder()
                 .fullParams(new Bm25Parameters(1.2, 0.5))
@@ -46,6 +49,12 @@ public class ResultRankingParameters {
                 .bm25FullWeight(1.)
                 .bm25PrioWeight(1.)
                 .tcfWeight(2.)
+                .temporalBias(TemporalBias.NONE)
+                .temporalBiasWeight(1. / (10.))
                 .build();
     }
+
+    public enum TemporalBias {
+        RECENT, OLD, NONE
+    };
 }
diff --git a/code/features-search/result-ranking/src/main/java/nu/marginalia/ranking/ResultValuator.java b/code/features-search/result-ranking/src/main/java/nu/marginalia/ranking/ResultValuator.java
index 73aa2c0d..69a379fb 100644
--- a/code/features-search/result-ranking/src/main/java/nu/marginalia/ranking/ResultValuator.java
+++ b/code/features-search/result-ranking/src/main/java/nu/marginalia/ranking/ResultValuator.java
@@ -1,7 +1,9 @@
 package nu.marginalia.ranking;
 
 import nu.marginalia.index.client.model.results.ResultRankingContext;
+import nu.marginalia.index.client.model.results.ResultRankingParameters;
 import nu.marginalia.index.client.model.results.SearchResultKeywordScore;
+import nu.marginalia.model.crawl.PubDate;
 import nu.marginalia.model.idx.DocumentMetadata;
 import nu.marginalia.ranking.factors.*;
 
@@ -52,21 +54,30 @@ public class ResultValuator {
         int asl = DocumentMetadata.decodeAvgSentenceLength(documentMetadata);
         int quality = DocumentMetadata.decodeQuality(documentMetadata);
         int topology = DocumentMetadata.decodeTopology(documentMetadata);
+        int year = DocumentMetadata.decodeYear(documentMetadata);
 
         double averageSentenceLengthPenalty = (asl >= rankingParams.shortSentenceThreshold ? 0 : -rankingParams.shortSentencePenalty);
 
-        double qualityPenalty = -quality * rankingParams.qualityPenalty;
-        double rankingBonus = (255. - rank) * rankingParams.domainRankBonus;
-        double topologyBonus = Math.log(1 + topology);
-        double documentLengthPenalty = length > rankingParams.shortDocumentThreshold ? 0 : -rankingParams.shortDocumentPenalty;
-
+        final double qualityPenalty = -quality * rankingParams.qualityPenalty;
+        final double rankingBonus = (255. - rank) * rankingParams.domainRankBonus;
+        final double topologyBonus = Math.log(1 + topology);
+        final double documentLengthPenalty = length > rankingParams.shortDocumentThreshold ? 0 : -rankingParams.shortDocumentPenalty;
+        final double temporalBias;
 
+        if (rankingParams.temporalBias == ResultRankingParameters.TemporalBias.RECENT) {
+            temporalBias = - Math.abs(year - PubDate.MAX_YEAR) * rankingParams.temporalBiasWeight;
+        } else if (rankingParams.temporalBias == ResultRankingParameters.TemporalBias.OLD) {
+            temporalBias = - Math.abs(year - PubDate.MIN_YEAR) * rankingParams.temporalBiasWeight;
+        } else {
+            temporalBias = 0;
+        }
 
         double overallPart = averageSentenceLengthPenalty
                            + documentLengthPenalty
                            + qualityPenalty
                            + rankingBonus
                            + topologyBonus
+                           + temporalBias
                            + priorityTermBonus.calculate(scores);
 
         for (int set = 0; set <= sets; set++) {