What Are We Optimizing For? A Human-centric Evaluation Of Deep Learning-based Recommender Systems

Deep learning-based (DL) models in recommender systems (RecSys) have gained significant recognition for their remarkable accuracy in predicting user preferences. However, their performance often lacks a comprehensive evaluation from a human-centric perspective, which encompasses various dimensions beyond simple interest matching. In this work, we have developed a robust human-centric evaluation framework that incorporates seven diverse metrics to assess the quality of recommendations generated by five recent open-sourced DL models. Our evaluation datasets consist of both offline benchmark data and personalized online recommendation feedback collected from 445 real users. We find that (1) different DL models have different pros and cons in the multi-dimensional metrics that we test with; (2) users generally want a combination of accuracy with at least one another human values in the recommendation; (3) the degree of combination of different values needs to be carefully experimented to user preferred level.