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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