Github user techaddict commented on a diff in the pull request:
https://github.com/apache/spark/pull/597#discussion_r12362778
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -88,7 +92,11 @@ object MovieLensALS {
val ratings = sc.textFile(params.input).map { line =>
val fields = line.split("::")
- Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
+ if (params.implicitPrefs) {
+ Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble - 2.5)
--- End diff --
```
/**
* MovieLens ratings are on a scale of 1-5:
* 5: Must see
* 4: Will enjoy
* 3: It's okay
* 2: Fairly bad
* 1: Awful
* So we should not recommend a movie if the predicted rating is
less than 3.
* To map ratings to confidence scores, we use
* 5 -> 2.5, 4 -> 1.5, 3 -> 0.5, 2 -> -0.5, 1 -> -1.5. This
mappings means unobserved
* entries are generally between It's okay and Fairly bad.
* The semantics of 0 in this expanded world of non-positive weights
* are "the same as never having interacted at all" -- which
doesn't quite fit.
* It's possible that 0 values are ignored when constructing the
sparse representation,
* because the 0s are implicit. This would be a problem, at least,
a theoretical one.
*/
```
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