Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/597#discussion_r12388703
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -88,7 +92,27 @@ 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) {
+ /**
+ * 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"
+ * It's possible that 0 values are ignored when constructing the
sparse representation,
--- End diff --
This sentence may be confusing to users. Shall we hide theory from users?
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