I did not write the template you are using. I am trying to explain what the template should be doing and how ALS works. I’m sure that with exactly the same data you should get the same results but in real life you will need to understand the algorithm a little deeper and so the pointer to the code that is being executed by the template from Spark MLlib. If this is not helpful please ignore the advice.
On Dec 22, 2017, at 11:16 AM, GMAIL <babaevka...@gmail.com> wrote: But I strictly followed the instructions from the site and did not change anything even. Everything I did was steps from this page. I did not perform any additional operations, including editing the source code. Instruction (Quick Start - Recommendation Engine Template): http://predictionio.incubator.apache.org/templates/recommendation/quickstart/ <http://predictionio.incubator.apache.org/templates/recommendation/quickstart/> 2017-12-22 22:12 GMT+03:00 Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>>: Implicit means you assign a score to the event based on your own guess. Explicit uses ratings the user makes. One score is a guess by you (like a 4 for buy) and the other is a rating made by the user. ALS comes in 2 flavors, one for explicit scoring, used to predict rating and the other for implicit scoring used to predict something the user will prefer. Make sure your template is using the explicit version of ALS. https://spark.apache.org/docs/2.2.0/ml-collaborative-filtering.html#explicit-vs-implicit-feedback <https://spark.apache.org/docs/2.2.0/ml-collaborative-filtering.html#explicit-vs-implicit-feedback> On Dec 21, 2017, at 11:09 PM, GMAIL <babaevka...@gmail.com <mailto:babaevka...@gmail.com>> wrote: I wanted to use the Recomender because I expected that it could predict the scores as it is done by MovieLens. And it seems to be doing so, but for some reason the input and output scale is different. In imported scores, from 1 to 5, and in the predicted from 1 to 10. If by implicit scores you mean events without parameters, then I am aware that in essence there is also an score. I watched the DataSource in Recommender and there were only two events: rate and buy. Rate takes an score, and the buy implicitly puts the rating at 4 (out of 5, as I think). And I still did not understand exactly where to look for me and what to correct, so that incoming and predicted estimates were on the same scale. 2017-12-19 4:10 GMT+03:00 Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>>: There are 2 types of MLlib ALS recommenders last I checked, implicit and explicit. Implicit ones you give any arbitrary score, like a 1 for purchase. The explicit one you can input ratings and it is expected to predict ratings for an individual. But both iirc also have a regularization parameter that affects the scoring and is a param so you have to experiment with it using cross-validation to see where you get the best results. There is an old metric used for this type of thing called RMSE (root-mean-square error) which, when minimized will give you scores that most closely match actual scores in the hold-out set (see wikipedia on cross-validation and RMSE). You may have to use explicit ALS and tweak the regularization param, to get the lowest RMSE. I doubt anything will guarantee them to be in exactly the range of ratings so you’ll then need to pick the closest rating. On Dec 18, 2017, at 10:42 AM, GMAIL <babaevka...@gmail.com <mailto:babaevka...@gmail.com>> wrote: That is, the predicted scores that the Recommender returns can not just be multiplied by two, but may be completely wrong? I can not, say, just divide the predictions by 2 and pretend that everything is fine? 2017-12-18 21:35 GMT+03:00 Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>>: The UR and the Recommendations Template use very different technology underneath. In general the scores you get from recommenders are meaningless on their own. When using ratings as numerical values with a ”Matrix Factorization” recommender like the ones in MLlib, upon which the Recommendations Template is based need to have a regularization parameter. I don’t know for sure but maybe this is why the results don’t come in the range of input ratings. I haven’t looked at the code in a long while. If you are asking about the UR it would not take numeric ratings and the scores cannot be compared to them. For many reasons that I have written about before I always warn people about using ratings, which have been discontinued as a source of input for Netflix (who have removed them from their UX) and many other top recommender users. There are many reasons for this, not the least of which is that they are ambiguous and don’t directly relate to whether a user might like an item. For instance most video sources now use something like the length of time a user watches a video, and review sites prefer “like” and “dislike”. The first is implicit and the second is quite unambiguous. On Dec 18, 2017, at 12:32 AM, GMAIL <babaevka...@gmail.com <mailto:babaevka...@gmail.com>> wrote: Does it seem to me or UR strongly differs from Recommender? At least I can't find method getRatings in class DataSource, which contains all events, in particular, "rate", that I needed. 2017-12-18 11:14 GMT+03:00 Noelia Osés Fernández <no...@vicomtech.org <mailto:no...@vicomtech.org>>: I didn't solve the problem :( Now I use the universal recommender On 18 December 2017 at 09:12, GMAIL <babaevka...@gmail.com <mailto:babaevka...@gmail.com>> wrote: And how did you solve this problem? Did you divide prediction score by 2? 2017-12-18 10:40 GMT+03:00 Noelia Osés Fernández <no...@vicomtech.org <mailto:no...@vicomtech.org>>: I got the same problem. I still don't know the answer to your question :( On 17 December 2017 at 14:07, GMAIL <babaevka...@gmail.com <mailto:babaevka...@gmail.com>> wrote: I thought that there was a 5 point scale, but if so, why do I get predictions of 7, 8, etc.? P.S. Sorry for my English. 2017-12-17 16:05 GMT+03:00 GMAIL <babaevka...@gmail.com <mailto:babaevka...@gmail.com>>: Hi. I train with Recommendation Engine Template. I use data from sample_movielens_data.txt and there all score less than 5, but I get prediction with score more than 5. 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