Incorporating multi-level positive feedback to session-based nearest-neighbor
Qian Zhang, Masahiro Sato, Sho Takemori, Tomoko Ohkuma · 2021
User feedback such as clicks, likes and follows is widely considered positive signal, as users infer their preference through these signals. However, a click feedback that is intended for a purchase indicates that the user's interest level is greater than that of a user who clicks just to view a page. Thus, compared with the unary positive feedback, some other types of user feedback are considered more reliable because of their ability to indicate the different preference levels of users. Some recommender systems focus on using such multiple-level positive feedback to develop model-based methods. In this paper, we take them into account to implement a nearest-neighbor method that demonstrates competitive performance for session-based recommendation. Herein, we present experimental evaluations on different domain datasets to demonstrate the benefits of our proposed method. Our results show that incorporating such multiple-level preferences of users with the neighbor-based model leads to an improvement in performance for session-based recommendation.