[1F5-OS-10c] OS-10

6:10 PM - 6:25 PM JST(9:10 AM - 9:25 AM UTC)

[1F5-OS-10c-03] Multi-step Behavior Prediction for Medium- to Long-term Anticipatory Recommendations

〇Tomoka Ikeda¹, Taichi Sakaguchi², Takashi Bando² (1. Panasonic Corporation, 2. Panasonic Holdings Corporation)

Keywords:

Behavior prediction

Approaches that proactively recommend the next item to purchase or the next action to take based on user behavior prediction have been proposed in recent years; however, because mainstream methods simultaneously optimize recommendation timing and content using only one-step-ahead behavior predictions, generating medium- to long-term sequences of recommended items has been difficult. To address this, we propose a multi-step behavior prediction model that introduces a profile representing users’ latent behavioral tendencies. We envisage an experience in which users can, at their chosen timing, review subsequent proposals all at once, and we focus on the stability of medium- to long-term predictions looking two or more steps ahead. The proposed method combines multi-horizon learning, which jointly learns multiple steps ahead, with latent profile representations to capture medium- to long-term behavioral transitions, aiming to have recommendations prepared when users need them. To validate its effectiveness, we conduct comparative experiments against existing methods based on one-step prediction and evaluate multi-step-ahead prediction performance as well as the evolution of uncertainty in future predictions.