We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based models that alleviate the quadratic complexity of self-attention, adversarial and hierarchical architectures. Our models outperform baselines on the ContentWise and RL4RS datasets and can be used in RS simulators to model user response for RS evaluation and pretraining.

翻译：我们开发并评估了用于建模推荐系统中用户行为的神经架构，其灵感来源于网络搜索中的点击模型，但超越了标准点击模型。提出的架构包括循环网络、基于Transformer的模型（缓解了自注意力机制的二次复杂度问题）、对抗性架构和层次化架构。我们的模型在ContentWise和RL4RS数据集上超越了基线模型，可用于推荐系统模拟器中，以建模用户响应，从而进行推荐系统评估和预训练。