Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
<|reference_start|>Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention: 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<|reference_end|>
@article{liu2024sequential,
    title={Sequential Recommendation for Optimizing Both Immediate Feedback and
  Long-term Retention},
    author={Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao,
  Kesen Zhao, Lantao Hu, Peng Jiang and Kun Gai},
    journal={arXiv preprint arXiv:2404.03637},
    year={2024},
    doi={10.1145/3626772.3657829},
    archivePrefix={arXiv},
    eprint={2404.03637},
    primaryClass={cs.IR}
}