Goal Modeling-based Evaluation for Personalized Recommendation Systems
Alaa Alslaity, Thomas Tran · 2021
Designing and evaluating a recommendation algorithm are typically user-centric operations. However, users are not the sole party of real-world applications. Therefore, designing, deploying, and evaluating a personalized system should consider all parties (or stakeholders). Dealing with recommender systems as multistakeholders systems is a relatively new research direction. In particular, considering the requirements of multiple stakeholders in selecting the best algorithm among a set of alternatives has not been discussed extensively. An adaptive evaluation approach that can handle personalized needs from all parties is therefore required. This paper aims to fill this gap by introducing the use of goal modeling to support the selection of a recommendation algorithm. Through an illustrative example, we show the feasibility of modeling the recommendation alternatives and their contributions to multiple stakeholders’ goals so that the selected algorithm is well-aligned with the overall system requirements. Accordingly, we say that the goal modeling approach has the potential of helping practitioners and researchers to better reason about algorithms selection and, therefore, advances the development of recommender systems.