AAAI 2025technical0 citations
Top-one Recommendation with Anonymous User Behaviors (Student Abstract)
Abstract
Top-one recommendation with anonymous user behaviors, also known as session-based recommendation (SBR), faces challenges of top-one ranking and short anonymous sequences. To this end, we propose a novel objective that combines (1) a reciprocal rank loss to directly optimize the benchmark metric of top-one recommendation, with (2) a listwise contrastive loss to handle short sequences through listwise augmented consistency regularization. Empirical studies demonstrate that optimizing the proposed objective significantly improves the performance of existing SBR baselines.
BibTeX
@article{Lu_Wu_2025, title={Top-one Recommendation with Anonymous User Behaviors (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35274}, DOI={10.1609/aaai.v39i28.35274}, abstractNote={Top-one recommendation with anonymous user behaviors, also known as session-based recommendation (SBR), faces challenges of top-one ranking and short anonymous sequences. To this end, we propose a novel objective that combines (1) a reciprocal rank loss to directly optimize the benchmark metric of top-one recommendation, with (2) a listwise contrastive loss to handle short sequences through listwise augmented consistency regularization. Empirical studies demonstrate that optimizing the proposed objective significantly improves the performance of existing SBR baselines.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lu, Xiangkui and Wu, Jun}, year={2025}, month={Apr.}, pages={29423-29425} }