This is a call for papers for the ACM TIST special issue on Benchmarking Recommender Systems.
Call for Papers
ACM Transactions on Intelligent Systems and Technology
Special Issue on Recommender System Benchmarking
Overview
Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these systems is still based on traditional information retrieval and statistics metrics, e.g. precision, recall, RMSE often not taking the use-case and situation of the system into consideration.
However, the rapid evolution of recommender systems in both their goals and their application domains foster the need for new evaluation methodologies and environments.