Abstrakt
Improvements in the recommender systems (RS) domain are not possible without a thorough way to evaluate and compare newly proposed approaches. User studies represent a viable alternative to online and offline evaluation schemes, but despite their numerous benefits, they are only rarely used.
One of the main reasons behind this fact is that preparing a user study from scratch involves a lot of extra work on top of a simple algorithm proposal. To simplify this task, we propose EasyStudy, a modular framework built on the credo "Make simple things fast and hard things possible".
It features ready-to-use datasets, preference elicitation methods, incrementally tuned baseline algorithms, study flow plugins, and evaluation metrics. As a result, a simple study comparing several RS can be deployed with just a few clicks, while more complex study designs can still benefit from a range of reusable components, such as preference elicitation.
Overall, EasyStudy dramatically decreases the gap between the laboriousness of offline evaluation vs. user studies and, therefore, may contribute towards the more reliable and insightful user-centric evaluation of next-generation RS. The project repository is available from https://bit.ly/easy-study-repo.