twitter · Lioness100 · Apr 8, 2023 · Apr 11, 2023
diff --git a/ann/src/main/java/com/twitter/ann/hnsw/DistancedItemQueue.java b/ann/src/main/java/com/twitter/ann/hnsw/DistancedItemQueue.java
@@ -18,7 +18,7 @@ public class DistancedItemQueue<U, T> implements Iterable<DistancedItem<T>> {
   private final PriorityQueue<DistancedItem<T>> queue;
   private final boolean minQueue;
   /**
-   * Creates ontainer for items with their distances.
+   * Creates container for items with their distances.
    *
    * @param origin Origin (reference) point
    * @param initial Initial list of elements to add in the structure
@@ -94,7 +94,7 @@ public DistancedItem<T> dequeue() {
   }
 
   /**
-   * Dequeue all the elements from queueu with ordering mantained
+   * Dequeue all the elements from queue with ordering maintained
    *
    * @return remove all the elements in the order of the queue i.e min/max queue.
    */

diff --git a/ann/src/main/scala/com/twitter/ann/annoy/AnnoyCommon.scala b/ann/src/main/scala/com/twitter/ann/annoy/AnnoyCommon.scala
@@ -36,7 +36,7 @@ object AnnoyCommon {
 
 case class AnnoyRuntimeParams(
   /* Number of vectors to evaluate while searching. A larger value will give more accurate results, but will take longer time to return.
-   * Default value would be numberOfTrees*numberOfNeigboursRequested
+   * Default value would be numberOfTrees*numberOfNeighboursRequested
    */
   nodesToExplore: Option[Int])
     extends RuntimeParams {

diff --git a/ann/src/main/scala/com/twitter/ann/annoy/RawAnnoyQueryIndex.scala b/ann/src/main/scala/com/twitter/ann/annoy/RawAnnoyQueryIndex.scala
@@ -102,9 +102,9 @@ private[this] class RawAnnoyQueryIndex[D <: Distance[D]](
   ): Future[List[NeighborWithDistance[Long, D]]] = {
     futurePool {
       val queryVector = embedding.toArray
-      val neigboursToRequest = neighboursToRequest(numOfNeighbours, runtimeParams)
-      val neigbours = index
-        .getNearestWithDistance(queryVector, neigboursToRequest)
+      val neighboursToRequest = neighboursToRequest(numOfNeighbours, runtimeParams)
+      val neighbours = index
+        .getNearestWithDistance(queryVector, neighboursToRequest)
         .asScala
         .take(numOfNeighbours)
         .map { nn =>
@@ -114,24 +114,24 @@ private[this] class RawAnnoyQueryIndex[D <: Distance[D]](
         }
         .toList
 
-      neigbours
+      neighbours
     }
   }
 
   // Annoy java lib do not expose param for numOfNodesToExplore.
-  // Default number is numOfTrees*numOfNeigbours.
+  // Default number is numOfTrees*numOfNeighbours.
   // Simple hack is to artificially increase the numOfNeighbours to be requested and then just cap it before returning.
   private[this] def neighboursToRequest(
     numOfNeighbours: Int,
     annoyParams: AnnoyRuntimeParams
   ): Int = {
     annoyParams.nodesToExplore match {
       case Some(nodesToExplore) => {
-        val neigboursToRequest = nodesToExplore / numOfTrees
-        if (neigboursToRequest < numOfNeighbours)
+        val neighboursToRequest = nodesToExplore / numOfTrees
+        if (neighboursToRequest < numOfNeighbours)
           numOfNeighbours
         else
-          neigboursToRequest
+          neighboursToRequest
       }
       case _ => numOfNeighbours
     }

diff --git a/ann/src/main/scala/com/twitter/ann/brute_force/BruteForceIndex.scala b/ann/src/main/scala/com/twitter/ann/brute_force/BruteForceIndex.scala
@@ -105,7 +105,7 @@ object SerializableBruteForceIndex {
 }
 
 /**
- * This is a class that wrapps a BruteForceIndex and provides a method for serialization.
+ * This is a class that wraps a BruteForceIndex and provides a method for serialization.
  *
   * @param bruteForceIndex all queries and updates are sent to this index.
  * @param embeddingInjection injection that can convert embeddings to thrift embeddings.

diff --git a/ann/src/main/scala/com/twitter/ann/hnsw/TypedHnswIndex.scala b/ann/src/main/scala/com/twitter/ann/hnsw/TypedHnswIndex.scala
@@ -18,7 +18,7 @@ object TypedHnswIndex {
    *                       construction, but better index quality. At some point, increasing
    *                       ef_construction does not improve the quality of the index. One way to
    *                       check if the selection of ef_construction was ok is to measure a recall
-   *                       for M nearest neighbor search when ef = ef_constuction: if the recall is
+   *                       for M nearest neighbor search when ef = ef_construction: if the recall is
    *                       lower than 0.9, than there is room for improvement.
    * @param maxM The number of bi-directional links created for every new element during construction.
    *             Reasonable range for M is 2-100. Higher M work better on datasets with high
@@ -64,7 +64,7 @@ object TypedHnswIndex {
    *                       construction, but better index quality. At some point, increasing
    *                       ef_construction does not improve the quality of the index. One way to
    *                       check if the selection of ef_construction was ok is to measure a recall
-   *                       for M nearest neighbor search when ef = ef_constuction: if the recall is
+   *                       for M nearest neighbor search when ef = ef_construction: if the recall is
    *                       lower than 0.9, than there is room for improvement.
    * @param maxM The number of bi-directional links created for every new element during construction.
    *             Reasonable range for M is 2-100. Higher M work better on datasets with high

diff --git a/ann/src/main/scala/com/twitter/ann/scalding/benchmark/Knn.scala b/ann/src/main/scala/com/twitter/ann/scalding/benchmark/Knn.scala
@@ -106,7 +106,7 @@ trait KnnJobBase {
 
 object KnnJob extends TwitterExecutionApp with KnnJobBase {
 
-  val KnnPathSuffix: String = "/user/cortex-mlx/qualatative_analysis/knn_ground_truth/"
+  val KnnPathSuffix: String = "/user/cortex-mlx/qualitative_analysis/knn_ground_truth/"
   val partitionKey: String = "version"
 
   override def job: Execution[Unit] = Execution.withId { implicit uniqueId =>

diff --git a/ann/src/main/scala/com/twitter/ann/scalding/offline/KnnHelper.scala b/ann/src/main/scala/com/twitter/ann/scalding/offline/KnnHelper.scala
@@ -158,7 +158,7 @@ object KnnHelper {
    * @param reducers number of reducers for grouping
    * @param isSearchSpaceLarger Used for optimization: Is the search space larger than the query space? Ignored if numOfSearchGroups > 1.
    * @param numOfSearchGroups we divide the search space into these groups (randomly). Useful when the search space is too large. Overrides isSearchSpaceLarger.
-   * @param numReplicas Each search group will be responsible for 1/numReplicas queryEmebeddings.
+   * @param numReplicas Each search group will be responsible for 1/numReplicas queryEmbeddings.
    *                    This might speed up the search when the size of the index embeddings is
    *                    large.
    * @tparam A type of query entity
@@ -226,7 +226,7 @@ object KnnHelper {
    *                          the search space is too large. Search groups are shards. Choose this
    *                          number by ensuring searchSpaceEmbeddings.size / numOfSearchGroups
    *                          embeddings will fit into memory.
-   * @param numReplicas Each search group will be responsible for 1/numReplicas queryEmebeddings.
+   * @param numReplicas Each search group will be responsible for 1/numReplicas queryEmbeddings.
    *                    By increasing this number, we can parallelize the work and reduce end to end
    *                    running times.
    * @param indexingStrategy How we will search for nearest neighbors within a search group

diff --git a/ann/src/main/scala/com/twitter/ann/scalding/offline/KnnTruthSetGenerator.scala b/ann/src/main/scala/com/twitter/ann/scalding/offline/KnnTruthSetGenerator.scala
@@ -12,7 +12,7 @@ import com.twitter.scalding.UniqueID
 import com.twitter.scalding_internal.job.TwitterExecutionApp
 
 /**
- * This job reads index embedding data, query embeddings data, and split into index set, query set and true nearest neigbor set
+ * This job reads index embedding data, query embeddings data, and split into index set, query set and true nearest neighbor set
  * from query to index.
  */
 object KnnTruthSetGenerator extends TwitterExecutionApp {

diff --git a/ann/src/main/scala/com/twitter/ann/scalding/offline/indexbuilder/README.rst b/ann/src/main/scala/com/twitter/ann/scalding/offline/indexbuilder/README.rst
@@ -58,7 +58,7 @@ This job builds an ANN index based on hnsw algorithm using producer embeddings (
 Job arguments
 *************
 
-Enviroment variables (resources):
+Environment variables (resources):
 ==============
 - **CPU** Number of cpu cores (default: 32)
 - **RAM_GB** RAM in gigabytes (default: 150)
@@ -95,7 +95,7 @@ General arguments (specified as **--profile.{options}**):
 - **num_dimensions** Dimension of embedding in the input data. An exception will be thrown if any entry does not have a number of dimensions equal to this number.
 - **metric** Distance metric (InnerProduct/Cosine/L2)
 - **concurrency_level** Specifies how many parallel inserts happen to the index. This should probably be set to the number of cores on the machine.
-- **algo** The kind of index you want to ouput. The supported options right now are:
+- **algo** The kind of index you want to output. The supported options right now are:
 
   1. **hnsw** (Metric supported: Cosine, L2, InnerProduct)
 

diff --git a/ann/src/main/scala/com/twitter/ann/service/loadtest/LoadTestRecorder.scala b/ann/src/main/scala/com/twitter/ann/service/loadtest/LoadTestRecorder.scala
@@ -179,7 +179,7 @@ class InMemoryLoadTestQueryRecorder[T](
     latencyHistogram.add(queryLatency.inMicroseconds)
     counter.incrementAndGet()
     // Requests are assumed to have started around the time time of the first time record was called
-    // plus the time it took for that query to hhave completed.
+    // plus the time it took for that query to have completed.
     val (elapsedSinceFirstCall, firstQueryLatency) = elapsedTimeFun.get()
     val durationSoFar = elapsedSinceFirstCall() + firstQueryLatency
     elapsedTime.set(durationSoFar)

diff --git a/ann/src/main/scala/com/twitter/ann/service/loadtest/README.md b/ann/src/main/scala/com/twitter/ann/service/loadtest/README.md
@@ -155,7 +155,7 @@ $ oscar hdfs \
     --sample_percent $SAMPLE_PERCENT
 ```
 
-It will sample 0.1% of embeddings and store them in `tab` format to hdfs that can be direcly used as `query_set` for loadtest.
+It will sample 0.1% of embeddings and store them in `tab` format to hdfs that can be directly used as `query_set` for loadtest.
 
 # Knn Truth Set Generator
 

diff --git a/ann/src/main/scala/com/twitter/ann/util/IndexBuilderUtils.scala b/ann/src/main/scala/com/twitter/ann/util/IndexBuilderUtils.scala
@@ -15,7 +15,7 @@ object IndexBuilderUtils {
     concurrencyLevel: Int
   ): Future[Int] = {
     val count = new AtomicInteger()
-    // Async stream allows us to procss at most concurrentLevel futures at a time.
+    // Async stream allows us to process at most concurrentLevel futures at a time.
     Future.Unit.before {
       val stream = AsyncStream.fromSeq(embeddings)
       val appendStream = stream.mapConcurrent(concurrencyLevel) { annEmbedding =>

diff --git a/ann/src/main/thrift/com/twitter/ann/common/ann_common.thrift b/ann/src/main/thrift/com/twitter/ann/common/ann_common.thrift
@@ -27,7 +27,7 @@ struct AnnoyIndexMetadata {
 
 struct AnnoyRuntimeParam {
   /* Number of vectors to evaluate while searching. A larger value will give more accurate results, but will take longer time to return.
-   * Default value would be numberOfTrees*numberOfNeigboursRequested
+   * Default value would be numberOfTrees*numberOfNeighboursRequested
    */
   1: optional i32 numOfNodesToExplore
 }

diff --git a/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/CrMixerServer.scala b/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/CrMixerServer.scala
@@ -180,7 +180,7 @@ class CrMixerServer extends ThriftServer with Mtls with HttpServer with HttpMtls
         TripCandidateStoreModule,
         TwiceClustersMembersStoreModule,
         TweetBasedQigSimilarityEngineModule,
-        TweetBasedTwHINSimlarityEngineModule,
+        TweetBasedTwHINSimilarityEngineModule,
         TweetBasedUnifiedSimilarityEngineModule,
         TweetBasedUserAdGraphSimilarityEngineModule,
         TweetBasedUserTweetGraphSimilarityEngineModule,

diff --git a/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/logging/ScribeLoggerUtils.scala b/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/logging/ScribeLoggerUtils.scala
@@ -1,7 +1,7 @@
 package com.twitter.cr_mixer.logging
 
 import com.twitter.cr_mixer.featureswitch.CrMixerImpressedBuckets
-import com.twitter.cr_mixer.thriftscala.ImpressesedBucketInfo
+import com.twitter.cr_mixer.thriftscala.ImpressedBucketInfo
 import com.twitter.finagle.stats.StatsReceiver
 import com.twitter.frigate.common.util.StatsUtil
 import com.twitter.logging.Logger
@@ -24,13 +24,13 @@ object ScribeLoggerUtils {
 
   private[logging] def getImpressedBuckets(
     scopedStats: StatsReceiver
-  ): Option[List[ImpressesedBucketInfo]] = {
+  ): Option[List[ImpressedBucketInfo]] = {
     StatsUtil.trackNonFutureBlockStats(scopedStats.scope("getImpressedBuckets")) {
       CrMixerImpressedBuckets.getAllImpressedBuckets.map { listBuckets =>
         val listBucketsSet = listBuckets.toSet
         scopedStats.stat("impressed_buckets").add(listBucketsSet.size)
         listBucketsSet.map { bucket =>
-          ImpressesedBucketInfo(
+          ImpressedBucketInfo(
             experimentId = bucket.experiment.settings.experimentId.getOrElse(-1L),
             bucketName = bucket.name,
             version = bucket.experiment.settings.version,

diff --git a/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/model/ModelConfig.scala b/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/model/ModelConfig.scala
@@ -45,7 +45,7 @@ object ModelConfig {
   val DebuggerDemo: String = "DebuggerDemo"
 
   // ColdStartLookalike - this is not really a model name, it is as a placeholder to
-  // indicate ColdStartLookalike candidate source, which is currently being pluged into
+  // indicate ColdStartLookalike candidate source, which is currently being plugged into
   // CustomizedRetrievalCandidateGeneration temporarily.
   val ColdStartLookalikeModelName: String = "ConsumersBasedUtgColdStartLookalike20220707"
 

diff --git a/...weetBasedTwHINSimlarityEngineModule.scala → ...eetBasedTwHINSimilarityEngineModule.scala b/...weetBasedTwHINSimlarityEngineModule.scala → ...eetBasedTwHINSimilarityEngineModule.scala
@@ -21,7 +21,7 @@ import com.twitter.cr_mixer.similarity_engine.SimilarityEngine.SimilarityEngineC
 import com.twitter.cr_mixer.thriftscala.SimilarityEngineType
 import com.twitter.finagle.memcached.{Client => MemcachedClient}
 
-object TweetBasedTwHINSimlarityEngineModule extends TwitterModule {
+object TweetBasedTwHINSimilarityEngineModule extends TwitterModule {
   @Provides
   @Named(ModuleNames.TweetBasedTwHINANNSimilarityEngine)
   def providesTweetBasedTwHINANNSimilarityEngine(

diff --git a/...c/main/scala/com/twitter/cr_mixer/similarity_engine/CertoTopicTweetSimilarityEngine.scala b/...c/main/scala/com/twitter/cr_mixer/similarity_engine/CertoTopicTweetSimilarityEngine.scala
@@ -68,7 +68,7 @@ object CertoTopicTweetSimilarityEngine {
   case class Query(
     topicId: TopicId,
     maxCandidates: Int,
-    certoScoreTheshold: Double)
+    certoScoreThreshold: Double)
 
   def fromParams(
     topicId: TopicId,
@@ -86,7 +86,7 @@ object CertoTopicTweetSimilarityEngine {
       Query(
         topicId = topicId,
         maxCandidates = maxCandidates,
-        certoScoreTheshold = params(TopicTweetParams.CertoScoreThresholdParam)
+        certoScoreThreshold = params(TopicTweetParams.CertoScoreThresholdParam)
       ),
       params
     )

diff --git a/...main/scala/com/twitter/cr_mixer/similarity_engine/ConsumerBasedWalsSimilarityEngine.scala b/...main/scala/com/twitter/cr_mixer/similarity_engine/ConsumerBasedWalsSimilarityEngine.scala
@@ -63,7 +63,7 @@ case class WalsStats(scope: String, scopedStats: StatsReceiver) {
 }
 
 // StatsMap maintains a mapping from Model's input signature to a stats receiver
-// The Wals model suports multiple input signature which can run different graphs internally and
+// The Wals model supports multiple input signature which can run different graphs internally and
 // can have a different performance profile.
 // Invoking StatsReceiver.stat() on each request can create a new stat object and can be expensive
 // in performance critical paths.

diff --git a/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/similarity_engine/SimilarityEngine.scala b/cr-mixer/server/src/main/scala/com/twitter/cr_mixer/similarity_engine/SimilarityEngine.scala
@@ -65,7 +65,7 @@ object SimilarityEngine extends Logging {
    * We need some further refactor work to make it more flexible.
    *
    * @param deciderConfig Gate the Engine by a decider. If specified,
-   * @param enableFeatureSwitch. DO NOT USE IT FOR NOW. It needs some refactorting. Please set it to None (SD-20268)
+   * @param enableFeatureSwitch. DO NOT USE IT FOR NOW. It needs some refactoring. Please set it to None (SD-20268)
    */
   case class GatingConfig(
     deciderConfig: Option[DeciderConfig],
@@ -107,7 +107,7 @@ object SimilarityEngine extends Logging {
    *
    * @param underlyingStore un-cached store implementation
    * @param keyPrefix       a prefix differentiates 2 stores if they share the same key space.
-   *                        e.x. 2 implementations of ReadableStore[UserId, Seq[Candidiate] ]
+   *                        e.x. 2 implementations of ReadableStore[UserId, Seq[Candidate] ]
    *                        can use prefix "store_v1", "store_v2"
    * @return                A ReadableStore with a MemCache wrapper
    */

diff --git a/cr-mixer/thrift/src/main/thrift/product_context.thrift b/cr-mixer/thrift/src/main/thrift/product_context.thrift
@@ -3,7 +3,7 @@ namespace java com.twitter.cr_mixer.thriftjava
 #@namespace strato com.twitter.cr_mixer
 
 struct HomeContext {
-	2: optional i32 maxResults // enabled for QuaityFactor related DDGs only
+	2: optional i32 maxResults // enabled for QualityFactor related DDGs only
 } (persisted='true', hasPersonalData='false')
 
 struct NotificationsContext {

diff --git a/cr-mixer/thrift/src/main/thrift/scribe.thrift b/cr-mixer/thrift/src/main/thrift/scribe.thrift
@@ -18,7 +18,7 @@ struct VITTweetCandidatesScribe {
   2: required i64 userId (personalDataType = 'UserId')
   3: required list<VITTweetCandidateScribe> candidates
   7: required product.Product product
-  8: required list<ImpressesedBucketInfo> impressedBuckets
+  8: required list<ImpressedBucketInfo> impressedBuckets
 } (persisted='true', hasPersonalData = 'true')
 
 struct VITTweetCandidateScribe {
@@ -34,7 +34,7 @@ struct GetTweetsRecommendationsScribe {
   3: required Result result
   4: optional i64 traceId
   5: optional PerformanceMetrics performanceMetrics
-  6: optional list<ImpressesedBucketInfo> impressedBuckets
+  6: optional list<ImpressedBucketInfo> impressedBuckets
 } (persisted='true', hasPersonalData = 'true')
 
 struct SourceSignal {
@@ -90,7 +90,7 @@ union Result {
   6: TopLevelApiResult topLevelApiResult
 } (persisted='true', hasPersonalData = 'true')
 
-struct ImpressesedBucketInfo {
+struct ImpressedBucketInfo {
   1: required i64 experimentId (personalDataType = 'ExperimentId')
   2: required string bucketName
   3: required i32 version
@@ -106,7 +106,7 @@ struct GetRelatedTweetsScribe {
   5: optional i64 guestId (personalDataType = 'GuestId')
   6: optional i64 traceId
   7: optional PerformanceMetrics performanceMetrics
-  8: optional list<ImpressesedBucketInfo> impressedBuckets
+  8: optional list<ImpressedBucketInfo> impressedBuckets
 } (persisted='true', hasPersonalData = 'true')
 
 struct RelatedTweetTopLevelApiResult {
@@ -118,7 +118,7 @@ struct RelatedTweetTopLevelApiResult {
 union RelatedTweetResult {
   1: RelatedTweetTopLevelApiResult relatedTweetTopLevelApiResult
   2: FetchCandidatesResult fetchCandidatesResult
-  3: PreRankFilterResult preRankFilterResult # results after seqential filters
+  3: PreRankFilterResult preRankFilterResult # results after sequential filters
   # if later we need rankResult, we can add it here
 } (persisted='true', hasPersonalData = 'true')
 
@@ -130,7 +130,7 @@ struct GetUtegTweetsScribe {
   3: required UtegTweetResult utegTweetResult
   4: optional i64 traceId
   5: optional PerformanceMetrics performanceMetrics
-  6: optional list<ImpressesedBucketInfo> impressedBuckets
+  6: optional list<ImpressedBucketInfo> impressedBuckets
 } (persisted='true', hasPersonalData = 'true')
 
 struct UtegTweetTopLevelApiResult {
@@ -153,7 +153,7 @@ struct GetAdsRecommendationsScribe {
   3: required AdsRecommendationsResult result
   4: optional i64 traceId
   5: optional PerformanceMetrics performanceMetrics
-  6: optional list<ImpressesedBucketInfo> impressedBuckets
+  6: optional list<ImpressedBucketInfo> impressedBuckets
 } (persisted='true', hasPersonalData = 'true')
 
 struct AdsRecommendationTopLevelApiResult {

diff --git a/...ommendations/common/candidate_sources/recent_engagement/RepeatedProfileVisitsSource.scala b/...ommendations/common/candidate_sources/recent_engagement/RepeatedProfileVisitsSource.scala
@@ -105,7 +105,7 @@ class RepeatedProfileVisitsSource @Inject() (
     val recommendationThreshold = params.getInt(RepeatedProfileVisitsParams.RecommendationThreshold)
     val bucketingThreshold = params.getInt(RepeatedProfileVisitsParams.BucketingThreshold)
 
-    // Get the list of repeatedly visited profilts. Only keep accounts with >= bucketingThreshold visits.
+    // Get the list of repeatedly visited profiles. Only keep accounts with >= bucketingThreshold visits.
     val repeatedVisitedAccountsStitch: Stitch[Map[Long, Int]] =
       getRepeatedVisitedAccounts(params, userId).map(_.filter(kv => kv._2 >= bucketingThreshold))
 

diff --git a/...n/scala/com/twitter/follow_recommendations/common/clients/strato/StratoClientModule.scala b/...n/scala/com/twitter/follow_recommendations/common/clients/strato/StratoClientModule.scala
@@ -56,7 +56,7 @@ object StratoClientModule extends TwitterModule {
   val WTFPostNuxFeaturesPath = "ml/featureStore/onboarding/wtfPostNuxFeatures.User"
   val ElectionCandidatesPath = "onboarding/electionAccounts"
   val UserUserGraphPath = "recommendations/userUserGraph"
-  val WtfDissmissEventsPath = "onboarding/wtfDismissEvents"
+  val WtfDismissEventsPath = "onboarding/wtfDismissEvents"
   val RelatableAccountsPath = "onboarding/userrecs/relatableAccounts"
   val ExtendedNetworkCandidatesPath = "search/account_search/extendedNetworkCandidatesMH"
   val LabeledNotificationPath = "frigate/magicrecs/labeledPushRecsAggregated.User"
@@ -234,7 +234,7 @@ object StratoClientModule extends TwitterModule {
       Unit,
       (Long, (Long, Long)),
       WhoToFollowDismissEventDetails
-    ](WtfDissmissEventsPath)
+    ](WtfDismissEventsPath)
 
   @Provides
   @Singleton

diff --git a/.../main/scala/com/twitter/follow_recommendations/common/constants/GuiceNamedConstants.scala b/.../main/scala/com/twitter/follow_recommendations/common/constants/GuiceNamedConstants.scala
@@ -19,7 +19,7 @@ object GuiceNamedConstants {
   final val TWO_HOP_RANDOM_WALK_FETCHER = "two_hop_random_walk_fetcher"
   final val USER_RECOMMENDABILITY_FETCHER = "user_recommendability_fetcher"
   final val USER_STATE_FETCHER = "user_state_fetcher"
-  final val UTT_ACCOUNT_RECOMMENDATIONS_FETCHER = "utt_account_recomendations_fetcher"
+  final val UTT_ACCOUNT_RECOMMENDATIONS_FETCHER = "utt_account_recommendations_fetcher"
   final val UTT_SEED_ACCOUNTS_FETCHER = "utt_seed_accounts_fetcher"
 
   final val ELECTION_CANDIDATES_FETCHER = "election_candidates_fetcher"