  In the landscape of Recommender System (RS) applications, reinforcement
learning (RL) has recently emerged as a powerful tool, primarily due to its
proficiency in optimizing long-term rewards. Nevertheless, it suffers from
instability in the learning process, stemming from the intricate interactions
among bootstrapping, off-policy training, and function approximation. Moreover,
in multi-reward recommendation scenarios, designing a proper reward setting
that reconciles the inner dynamics of various tasks is quite intricate. In
response to these challenges, we introduce DT4IER, an advanced decision
transformer-based recommendation model that is engineered to not only elevate
the effectiveness of recommendations but also to achieve a harmonious balance
between immediate user engagement and long-term retention. The DT4IER applies
an innovative multi-reward design that adeptly balances short and long-term
rewards with user-specific attributes, which serve to enhance the contextual
richness of the reward sequence ensuring a more informed and personalized
recommendation process. To enhance its predictive capabilities, DT4IER
incorporates a high-dimensional encoder, skillfully designed to identify and
leverage the intricate interrelations across diverse tasks. Furthermore, we
integrate a contrastive learning approach within the action embedding
predictions, a strategy that significantly boosts the model's overall
performance. Experiments on three real-world datasets demonstrate the
effectiveness of DT4IER against state-of-the-art Sequential Recommender Systems
(SRSs) and Multi-Task Learning (MTL) models in terms of both prediction
accuracy and effectiveness in specific tasks. The source code is accessible
online to facilitate replication
