Examples and best practices for building recommendation systems
1–9 of 9 posts
Re: Examples and best practices for building recommendation systems
#2>Recall@k is a metric evaluate how many items, in the recommendation list, are relevant (hit) in the ground-truth data.
Re: Examples and best practices for building recommendation systems
#3Re: Examples and best practices for building recommendation systems
#4this really should've been named "recommendations for building recommendation systems"
Re: Examples and best practices for building recommendation systems
#5If you're the author, I'm not sure that the definition of recall@k is correct. >Recall@k is a metric evaluate how many items, in the recommendation list, are relevant (hit) in the ground-truth data.
Re: Examples and best practices for building recommendation systems
#6Also worth mention about implicit feedback
Re: Examples and best practices for building recommendation systems
#7Sadly but evaluation metrics are only implemented in pyspark.mllib (RDD API) but not in pyspark.ml (Dataframe API) Also worth mention about implicit feedback
Re: Examples and best practices for building recommendation systems
#8this really should've been named "recommendations for building recommendation systems"
hey one of the authors here. We are planning to create some notebooks on recommendation about recommendation algos :-)