diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala | 8 |
1 files changed, 7 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index f1ae7b85b4..cc56fd6ef2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -507,6 +507,9 @@ class ALS private ( val tempXtX = DoubleMatrix.zeros(triangleSize) val fullXtX = DoubleMatrix.zeros(rank, rank) + // Count the number of ratings each user gives to provide user-specific regularization + val numRatings = Array.fill(numUsers)(0) + // Compute the XtX and Xy values for each user by adding products it rated in each product // block for (productBlock <- 0 until numProductBlocks) { @@ -519,6 +522,7 @@ class ALS private ( if (implicitPrefs) { var i = 0 while (i < us.length) { + numRatings(us(i)) += 1 // Extension to the original paper to handle rs(i) < 0. confidence is a function // of |rs(i)| instead so that it is never negative: val confidence = 1 + alpha * abs(rs(i)) @@ -534,6 +538,7 @@ class ALS private ( } else { var i = 0 while (i < us.length) { + numRatings(us(i)) += 1 userXtX(us(i)).addi(tempXtX) SimpleBlas.axpy(rs(i), x, userXy(us(i))) i += 1 @@ -550,9 +555,10 @@ class ALS private ( // Compute the full XtX matrix from the lower-triangular part we got above fillFullMatrix(userXtX(index), fullXtX) // Add regularization + val regParam = numRatings(index) * lambda var i = 0 while (i < rank) { - fullXtX.data(i * rank + i) += lambda + fullXtX.data(i * rank + i) += regParam i += 1 } // Solve the resulting matrix, which is symmetric and positive-definite |