This paper jointly addresses the problem of data uncertainty, popularity bias, and exposure bias in session-based recommender systems. We study the symptoms of this bias both in item embeddings and in recommendations. We propose treating user interest as a stochastic process in the latent space and providing a model-agnostic implementation of this mathematical concept. The proposed stochastic component consists of elements: debiasing item embeddings with regularization for embedding uniformity, modeling dense user interest from session prefixes, and introducing fake targets in the data to simulate extended exposure. We conducted computational experiments on two popular benchmark datasets, Diginetica and YooChoose 1/64, as well as several modifications of the YooChoose dataset with different ratios of popular items. The results show that the proposed approach allows us to mitigate the challenges mentioned.

本研究针对基于会话的推荐系统中的数据不确定性、流行度偏差和曝光偏差等问题，提出将用户兴趣视为潜在空间中的随机过程。通过去偏项目嵌入、建模用户兴趣和引入虚假目标，我们的方法在多个数据集的实验中显示出有效减轻这些偏差的潜力。

基于会话的推荐系统：潜在空间中用户兴趣的随机过程