Search Results for author:
Found 4 papers, 2 papers with code
From Variability to Stability: Advancing RecSys Benchmarking Practices
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets.
Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?
In addition, we show that SASRec could be effectively trained with negative sampling and still outperform BERT4Rec, but the number of negative examples should be much larger than one.
The Long Tail of Context: Does it Exist and Matter?
Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person.
Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?
These findings highlight yet another difficulty in recommender system evaluation and call for a more detailed description of evaluation protocols.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
