I’m using ALS with spark 1.0.0, the code should be: https://github.com/apache/spark/blob/branch-1.0/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
I think the following two method should produce the same (or near) result: MatrixFactorizationModel model = ALS.train(ratings.rdd(), 30, 30, 0.01, -1, 1); MatrixFactorizationModel model = ALS.trainImplicit(ratings.rdd(), 30, 30, 0.01, -1, 0, 1); the data I used is display log, the format of log is as following: user item if-click I use 1.0 as score for click pair, and 0 as score for non-click pair. in the second method, the alpha is set to zero, so the confidence for positive and negative are both 1.0 (right?) I think the two method should produce similar result, but the result is : the second method’s result is very bad (the AUC of the first result is 0.7, but the AUC of the second result is only 0.61) I could not understand why, could you help me? Thank you very much! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/different-result-from-implicit-ALS-with-explicit-ALS-tp21823.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
