{"ID":2893962,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12202","arxiv_id":"2507.12202","title":"Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control","abstract":"Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important research question, as a better understanding of their internals can help understand, influence, and control their behavior, which is very important in a variety of real-world applications. Recently, sparse autoencoders (SAE) have been shown to be a promising unsupervised approach to extract interpretable features from neural networks. In this work, we extend SAE to sequential recommender systems and propose a framework for interpreting and controlling model representations. We show that this approach can be successfully applied to the transformer trained on a sequential recommendation task: directions learned in such an unsupervised regime turn out to be more interpretable and monosemantic than the original hidden state dimensions. Further, we demonstrate a straightforward way to effectively and flexibly control the model's behavior, giving developers and users of recommendation systems the ability to adjust their recommendations to various custom scenarios and contexts.","short_abstract":"Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important research question, as a better understanding of their internals can help understand, influence, and control their behavior, which is very im...","url_abs":"https://arxiv.org/abs/2507.12202","url_pdf":"https://arxiv.org/pdf/2507.12202v2","authors":"[\"Anton Klenitskiy\",\"Konstantin Polev\",\"Daria Denisova\",\"Alexey Vasilev\",\"Dmitry Simakov\",\"Gleb Gusev\"]","published":"2025-07-16T12:57:43Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}