1
votes

I am trying to do some great Event recommandation using mahout.

For practice I tried following example-

https://github.com/RevBooyah/Static-mahout-recommender-tutorial/blob/master/ItemRecommend.java

I have some doubt that there are 3 things that used in data model UserId, ItemId and Preference as below-

enter image description here

But when I run the code with or without Preferences tha results are same, So my doubt is that what is the use of the Preferences ? If here it is useless then how can it be used for better Recommandation ?

I tried to find it but found nothing.

Can anyone please help me ?

1

1 Answers

1
votes

Are you using Tanimoto similarity of the Log Likelihood Ratio? The sample code uses Tanimoto and so should show different recommendation strengths depending on preference strengths. That will attempt to do something like predicting a user's ratings. It won't affect all weights so to test you might want to randomly assign weights and compare to the sample data. But it's not really important enough to bother with IMO.

This is an old method that dates back to when Netflix and others thought they wanted to guess at your item ratings. Netflix and most others have moved away from that because it is really much more important to rank correctly so the user gets the right set of recs in the best order.

Ranking is always better when using the Log Likelihood similarity measure--on all data I've seen and I've measured the difference in quality several times. LLR ignores the preference strength and calculates the recommendations based on a probabilistic method trying to predict what the user is most likely to prefer.

Ted Dunning describes LLR here