Streamsight: a toolkit for offline evaluation of recommender systems - Ng, Tze Kean (Nanyang Technological University, 2024)
There have been numerous Recommender System (RS) toolkits for offline evaluation that have been released over the years. However, little emphasis has been placed on observing the temporal aspects in the framework of these toolkits. We noticed that current toolkits tend to prioritize complex algorithm implementations and the variety of metrics that are used to evaluate these algorithms. Instead, we would like to take a step back to consider another angle of approaching the implementation of toolkits for RS. That is, to consider appropriate approaches in handling the temporal aspects of the data pertaining to the data split scheme and how it can be observed during the evaluation of RS.