Session-based recommendation, aiming at making the prediction of the user's next item click based on the information in a single session only even in the presence of some random user's behavior, is a complex problem. This complex problem requires a high-capability model of predicting the user's next action. Most (if not all) existing models follow the encoder-predictor paradigm where all studies focus on how to optimize the encoder module extensively in the paradigm but they ignore how to optimize the predictor module. In this paper, we discover the existing critical issue of the low-capability predictor module among existing models. Motivated by this, we propose a novel framework called \emph{\underline{S}ession-based \underline{R}ecommendation with \underline{Pred}ictor \underline{A}dd-\underline{O}n} (SR-PredictAO). In this framework, we propose a high-capability predictor module which could alleviate the effect of random user's behavior for prediction. It is worth mentioning that this framework could be applied to any existing models, which could give opportunities for further optimizing the framework. Extensive experiments on two real benchmark datasets for three state-of-the-art models show that \emph{SR-PredictAO} out-performs the current state-of-the-art model by up to 2.9\% in HR@20 and 2.3\% in MRR@20. More importantly, the improvement is consistent across almost all the existing models on all datasets, which could be regarded as a significant contribution in the field.

翻译：会话推荐旨在仅基于单个会话中的信息预测用户的下一次点击行为，即使在存在用户随机行为的情况下也能完成该任务，这是一个复杂的问题。该复杂问题需要高能力模型来预测用户的下一个动作。现有模型（若非全部，也是绝大多数）遵循编码器-预测器范式，所有研究均聚焦于如何在该范式中优化编码器模块，却忽视了预测器模块的优化。本文发现现有模型中预测器模块能力不足这一关键问题。受此启发，我们提出了一种新颖框架，称为基于会话推荐的高能力预测器附加模块（SR-PredictAO）。在该框架中，我们提出了一种高能力预测器模块，能够减轻用户随机行为对预测的影响。值得一提的是，该框架可应用于任何现有模型，为进一步优化框架提供了可能。在两个真实基准数据集上针对三种最先进模型的大量实验表明，与当前最先进模型相比，SR-PredictAO在HR@20上最高提升2.9%，在MRR@20上最高提升2.3%。更重要的是，该改进在几乎所有现有模型及所有数据集上均一致有效，这将被视为该领域的重要贡献。