predictionio not producing any predictions

Question

I am trying to test out prediction-io for the first time. I followed the installation instructions for linux and developed several test engines. After repeatedly getting the following error on my own datasets I decided to follow the movie 100k tutorial (https://github.com/PredictionIO/PredictionIO-Docs/blob/cbca03b1c2bad949db951a3a798f0080c48b3674/source/tutorials/movie-recommendation.rst). The same error seems to persist even though it seems as if my Hadoop is running correctly (and not in safe mode) and the engine says that it is running and training is complete. The error that I am getting is:

predictionio.ItemRecNotFoundError: request: GET /engines/itemrec/movie-rec/topn.json {'pio_n': 10, 'pio_uid': '28', 'pio_appkey': 'UsZmneFir39GXO9hID3wDhDQqYNje4S9Ea3jiQjrpHFzHwMEqCqwJKhtAziveC9D'} /engines/itemrec/movie-rec/topn.json?pio_n=10&pio_uid=28&pio_appkey=UsZmneFir39GXO9hID3wDhDQqYNje4S9Ea3jiQjrpHFzHwMEqCqwJKhtAziveC9D status: 404 body: {"message":"Cannot find recommendation for user."}

The rest of the tutorial runs as expected, just no predictions ever seem to appear. Can someone please point me in the right direction on how to solve this issue?

Thanks!

Answer 1

Several suggestions:

1. Check if there is data in PredictioIO's database. I saw jobs failing because there was some items in database but no users and no user-to-item actions. Look into Mongo database appdata - there should be collections named users, items and u2iActions. These collections are only created when you add first user-item-u2iaction there via API. That's bad that it is not clear whether job completed successfully or not via the web interface.

2. Check logs - PredictionIO logs, and Hadoop logs if you use Hadoop jobs. See if model training jobs did complete (BTW, did you invoke "Train prediction model now" via web interface?)

3. Verify if there is some data in predictionio_modeldata for your algorithm.

4. Well, even if model is trained OK, there can still be not enough data to produce recommendations for some user. Try "Random" to get the simplest recommendations available for all, to check if system as a whole works.