SunYatong on 4.0.0-alpha
add new branch 4.0.0-alpha (compare)
liweigu on 3.0.0
update Merge pull request #312 from li… (compare)
SunYatong on 3.0.0
fix ndcg (compare)
I have a user-item-rating data set (1 is low and 5 is high rating) and would like to obtain top-N recommendations. Currently, I am using the EALSRecommender. Here are my two questions:
1) Since I'm doing ranking, do I need to convert ratings into rankings for each user? I don't think so, but I want to make sure.
2) I am using K-fold cross-validation and AveragePrecision@10 to measure the performance of the model. Does KCV make sense when ranking items or should I use some other approach (e.g., LOOCV)? Also, should I use NDCG instead of AP? (I read somewhere that AP is more appropriate when having binary ratings)
Thanks in advance and thanks to @SunYatong for answering my questions on Github :-)
I am trying to evaluate the diversity of both, the slim and the plsa recommenders. however, the only result that I saw till now is 0.0. I wonder if there are special settings for diversity evaluator? bellow is the setting file.
Thanks in advance.
I wonder what tools and algorithms of Librec should I use if I want to implement a multi criteria similarity search for a bike recommendation system?
Assume I have a database where all bikes have 3 attributes: Category, Weight, Price.
The user sets his preferences to: Mountain Bike, 30kg, $200
I would like to get as a recommendation result the following 2 bikes:
So, how should I go about it using Librec?
Thanks in advance!