LR、FM模型

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score/VlogRovFMScorer.java

@@ -15,7 +15,7 @@ import java.util.*;
 import java.util.concurrent.*;
 
 
-public class VlogRovFMScorer extends BaseLRV2ModelScorer {
+public class VlogRovFMScorer extends BaseFMModelScorer {
 
     private static final int LOCAL_TIME_OUT = 150;
     private final static Logger LOGGER = LoggerFactory.getLogger(VlogRovFMScorer.class);

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score/VlogRovLRScorer.java

@@ -148,7 +148,7 @@ public class VlogRovLRScorer extends BaseLRV2ModelScorer {
         if (MapUtils.isNotEmpty(featureMap)) {
             try {
                 pro = lrModel.score(featureMap);
-                LOGGER.info("fea : {}, score:{}", JSONUtils.toJson(featureMap), pro);
+                // LOGGER.info("fea : {}, score:{}", JSONUtils.toJson(featureMap), pro);
             } catch (Exception e) {
                 LOGGER.error("score error for doc={} exception={}", item.getVideoId(), ExceptionUtils.getFullStackTrace(e));
             }

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score/model/FMModel.java

@@ -110,6 +110,9 @@ public class FMModel extends Model {
         while ((line = input.readLine()) != null) {
             String[] items = line.split("\t");
             if (items.length < 9) {
+                if (items[0].equals("bias")) {
+                    putFeature(model, items);
+                }
                 continue;
             }