# Cross-Representation Knowledge Transfer for Improved Sequential Recommendations

arXiv: https://arxiv.org/abs/2602.23471 · HTML: https://mlsanity.com/paper/2602.23471/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2109.12843/) (2021)
- [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?](https://mlsanity.com/paper/2309.07602/) (2023)
- [CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention](https://mlsanity.com/paper/2204.06519/) (2022)
- [Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations](https://mlsanity.com/paper/2408.12008/) (2024)
- [Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs](https://mlsanity.com/paper/2409.18721/) (2024)
- [Contrastive Self-supervised Learning in Recommender Systems: A Survey](https://mlsanity.com/paper/2303.09902/) (2023)
- [SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/2405.20878/) (2024)
- [RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders](https://mlsanity.com/paper/2408.02354/) (2024)
- [Multi-level Contrastive Learning Framework for Sequential Recommendation](https://mlsanity.com/paper/2208.13007/) (2022)
- [Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval](https://mlsanity.com/paper/2505.22238/) (2025)
- [LightGNN: Simple Graph Neural Network for Recommendation](https://mlsanity.com/paper/2501.03228/) (2025)
- [A Contextual-Aware Position Encoding for Sequential Recommendation](https://mlsanity.com/paper/2502.09027/) (2025)
- [DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation](https://mlsanity.com/paper/2508.18442/) (2025)
- [Ultra Fast Warm Start Solution for Graph Recommendations](https://mlsanity.com/paper/2509.01549/) (2025)
- [End-to-End Graph-Sequential Representation Learning for Accurate Recommendations](https://mlsanity.com/paper/2403.00895/) (2024)
- [Barlow Twins for Sequential Recommendation](https://mlsanity.com/paper/2510.26407/) (2025)

## Abstract (arXiv)

Transformer architectures, capable of capturing sequential dependencies in the history of user interactions, have become the dominant approach in sequential recommender systems. Despite their success, such models consider sequence elements in isolation, implicitly accounting for the complex relationships between them. Graph neural networks, in contrast, explicitly model these relationships through higher order interactions but are often unable to adequately capture their evolution over time, limiting their use for predicting the next interaction. To fill this gap, we present a new framework that combines transformers and graph neural networks and aligns different representations for solving next-item prediction task. Our solution simultaneously encodes structural dependencies in the interaction graph and tracks their dynamic change. Experimental results on a number of open datasets demonstrate that the proposed framework consistently outperforms both pure sequential and graph approaches in terms of recommendation quality, as well as recent methods that combine both types of signals.