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# Recommendation
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Collaborative filtering algorithms for building recommendation engines, including matrix factorization techniques optimized for large-scale user-item interaction datasets.
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## Capabilities
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### Alternating Least Squares (ALS)
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Matrix factorization algorithm using alternating least squares optimization for collaborative filtering recommendation systems.
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```scala { .api }
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/**
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 * Alternating Least Squares collaborative filtering
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 */
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class ALS extends Estimator[ALSModel] with ALSParams {
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  def setRank(value: Int): this.type
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  def setMaxIter(value: Int): this.type
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  def setRegParam(value: Double): this.type
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  def setImplicitPrefs(value: Boolean): this.type
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  def setAlpha(value: Double): this.type
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  def setUserCol(value: String): this.type
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  def setItemCol(value: String): this.type
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  def setRatingCol(value: String): this.type
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  def setPredictionCol(value: String): this.type
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  def setNonnegative(value: Boolean): this.type
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  def setCheckpointInterval(value: Int): this.type
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  def setSeed(value: Long): this.type
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  def setNumUserBlocks(value: Int): this.type
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  def setNumItemBlocks(value: Int): this.type
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  def setIntermediateRDDStorageLevel(value: StorageLevel): this.type
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  def setFinalRDDStorageLevel(value: StorageLevel): this.type
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  def setColdStartStrategy(value: String): this.type
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  def setBlockSize(value: Int): this.type
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}
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class ALSModel extends Model[ALSModel] with ALSParams {
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  def rank: Int
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  def userFactors: DataFrame
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  def itemFactors: DataFrame
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  def recommendForAllUsers(numItems: Int): DataFrame
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  def recommendForAllItems(numUsers: Int): DataFrame
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  def recommendForUserSubset(dataset: Dataset[_], numItems: Int): DataFrame
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  def recommendForItemSubset(dataset: Dataset[_], numUsers: Int): DataFrame
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}
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```
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**Usage Example:**
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```scala
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import org.apache.spark.ml.recommendation.ALS
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// Prepare training data from ratings
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val Array(training, test) = ratings.randomSplit(Array(0.8, 0.2))
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// Build the recommendation model using ALS on the training data
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val als = new ALS()
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  .setMaxIter(5)
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  .setRegParam(0.01)
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  .setUserCol("userId")
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  .setItemCol("movieId")
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  .setRatingCol("rating")
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  .setColdStartStrategy("drop")
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val model = als.fit(training)
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// Evaluate the model by computing the RMSE on the test data
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val predictions = model.transform(test)
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val evaluator = new RegressionEvaluator()
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  .setMetricName("rmse")
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  .setLabelCol("rating")
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  .setPredictionCol("prediction")
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val rmse = evaluator.evaluate(predictions)
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println(s"Root-mean-square error = $rmse")
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// Generate top 10 movie recommendations for each user
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val userRecs = model.recommendForAllUsers(10)
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userRecs.show()
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// Generate top 10 user recommendations for each movie
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val movieRecs = model.recommendForAllItems(10)
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movieRecs.show()
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```
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### Advanced ALS Features
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Extended functionality for handling various recommendation scenarios and optimization strategies.
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```scala { .api }
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/**
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 * ALS model with advanced recommendation capabilities
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 */
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class ALSModel extends Model[ALSModel] with ALSParams {
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  /**
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   * Recommend items for a specific user
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   */
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  def recommendForUsers(users: Dataset[_], numItems: Int): DataFrame
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  /**
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   * Recommend users for specific items
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   */
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  def recommendForItems(items: Dataset[_], numUsers: Int): DataFrame
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  /**
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   * Transform dataset with predictions
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   */
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  override def transform(dataset: Dataset[_]): DataFrame
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  /**
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   * Access to latent factor matrices
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   */
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  def userFactors: DataFrame
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  def itemFactors: DataFrame
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  /**
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   * Model persistence
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   */
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  override def write: MLWriter
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}
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/**
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 * Rating data structure for recommendations
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 */
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case class Rating(user: Int, item: Int, rating: Float) extends Product with Serializable
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```
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### Implicit Feedback Handling
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Specialized configurations for handling implicit feedback data where ratings represent confidence rather than explicit preferences.
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```scala { .api }
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/**
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 * ALS configuration for implicit feedback scenarios
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 */
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object ALSImplicit {
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  /**
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   * Create ALS instance optimized for implicit feedback
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   */
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  def create(): ALS = {
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    new ALS()
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      .setImplicitPrefs(true)
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      .setAlpha(1.0)  // Confidence scaling parameter
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      .setNonnegative(true)  // Ensure non-negative factors
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  }
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  /**
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   * Preprocess implicit feedback data
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   */
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  def preprocessImplicitData(
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    interactions: DataFrame,
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    userCol: String = "userId",
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    itemCol: String = "itemId",
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    countCol: String = "count"
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  ): DataFrame
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}
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```
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### Cold Start Strategies
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Methods for handling new users or items that weren't present during training.
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```scala { .api }
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/**
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 * Cold start handling strategies
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 */
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object ColdStartStrategy {
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  val NaN = "nan"      // Return NaN for cold start cases
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  val DROP = "drop"    // Drop cold start cases from predictions
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}
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/**
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 * Cold start recommendation utilities
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 */
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object ColdStartRecommender {
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  /**
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   * Generate recommendations for new users based on popular items
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   */
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  def recommendForNewUsers(
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    model: ALSModel,
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    popularItems: DataFrame,
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    numRecommendations: Int
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  ): DataFrame
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  /**
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   * Generate recommendations for new items based on similar items
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   */
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  def recommendForNewItems(
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    model: ALSModel,
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    itemFeatures: DataFrame,
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    numRecommendations: Int
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  ): DataFrame
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}
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```
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### Recommendation Evaluation
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Specialized evaluation metrics for recommendation systems.
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```scala { .api }
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/**
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 * Recommendation-specific evaluation metrics
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 */
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class RecommendationEvaluator extends Evaluator {
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  def setUserCol(value: String): this.type
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  def setItemCol(value: String): this.type
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  def setRatingCol(value: String): this.type
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  def setPredictionCol(value: String): this.type
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  def setMetricName(value: String): this.type
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  def setK(value: Int): this.type
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  def setColdStartStrategy(value: String): this.type
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}
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/**
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 * Ranking-based evaluation for recommendations
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 */
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object RecommendationMetrics {
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  /**
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   * Calculate Mean Average Precision at K
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   */
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  def meanAveragePrecisionAtK(
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    predictions: DataFrame,
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    k: Int,
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    userCol: String = "userId",
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    itemCol: String = "itemId",
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    ratingCol: String = "rating"
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  ): Double
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  /**
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   * Calculate Normalized Discounted Cumulative Gain at K
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   */
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  def ndcgAtK(
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    predictions: DataFrame,
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    k: Int,
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    userCol: String = "userId",
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    itemCol: String = "itemId",
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    ratingCol: String = "rating"
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  ): Double
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  /**
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   * Calculate precision and recall at K
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   */
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  def precisionRecallAtK(
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    predictions: DataFrame,
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    k: Int,
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    userCol: String = "userId",
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    itemCol: String = "itemId",
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    ratingCol: String = "rating"
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  ): (Double, Double)
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}
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```
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### Hyperparameter Tuning for ALS
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Specialized parameter tuning strategies for collaborative filtering models.
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```scala { .api }
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/**
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 * ALS hyperparameter tuning utilities
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 */
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object ALSTuning {
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  /**
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   * Create parameter grid for ALS tuning
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   */
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  def createParamGrid(
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    als: ALS,
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    ranks: Array[Int] = Array(10, 50, 100),
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    regParams: Array[Double] = Array(0.01, 0.1, 1.0),
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    alphas: Array[Double] = Array(1.0, 10.0, 40.0)
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  ): Array[ParamMap]
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  /**
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   * Cross-validation for recommendation systems with temporal splitting
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   */
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  def temporalCrossValidation(
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    als: ALS,
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    data: DataFrame,
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    evaluator: Evaluator,
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    paramGrid: Array[ParamMap],
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    timestampCol: String = "timestamp"
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  ): CrossValidatorModel
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}
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```
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### Distributed Training Optimization
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Configuration options for optimizing ALS training on large-scale distributed datasets.
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```scala { .api }
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/**
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 * ALS distributed training configuration
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 */
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object ALSDistributedTraining {
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  /**
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   * Optimize ALS for large-scale distributed training
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   */
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  def optimizeForScale(
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    als: ALS,
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    numUsers: Long,
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    numItems: Long,
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    numPartitions: Int
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  ): ALS = {
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    val userBlocks = math.min(numPartitions, math.sqrt(numUsers).toInt)
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    val itemBlocks = math.min(numPartitions, math.sqrt(numItems).toInt)
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    als
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      .setNumUserBlocks(userBlocks)
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      .setNumItemBlocks(itemBlocks)
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      .setIntermediateRDDStorageLevel(StorageLevel.MEMORY_AND_DISK)
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      .setFinalRDDStorageLevel(StorageLevel.MEMORY_AND_DISK)
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      .setCheckpointInterval(10)
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  }
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  /**
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   * Memory-optimized configuration for resource-constrained environments
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   */
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  def optimizeForMemory(als: ALS): ALS = {
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    als
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      .setIntermediateRDDStorageLevel(StorageLevel.DISK_ONLY)
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      .setFinalRDDStorageLevel(StorageLevel.DISK_ONLY)
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      .setCheckpointInterval(5)
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  }
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}
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```
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## Types
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```scala { .api }
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// Recommendation system imports
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import org.apache.spark.ml.recommendation._
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import org.apache.spark.sql.{DataFrame, Dataset}
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import org.apache.spark.storage.StorageLevel
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// ALS-specific types
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import org.apache.spark.ml.recommendation.{ALS, ALSModel}
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// Rating data structure
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case class Rating(user: Int, item: Int, rating: Float)
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// Parameter traits
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import org.apache.spark.ml.param.shared._
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// Evaluation imports
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import org.apache.spark.ml.evaluation.RegressionEvaluator
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import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
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```