arXiv:2606. 01670v1 Announce Type: cross Abstract: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs).
By Bangguo Zhu, Peng Huo, Yuanbo Zhao, Zhicheng Du, Jun Yin, Senzhang Wang
arXiv:2606. 14260v1 Announce Type: cross Abstract: Semantic IDs are crucial in generative recommendation, but with a fundamental limitation: temporal information is not well incorporated into semantic IDs.
By Dongdong Nian, Dongqi Fu, Chenliang Xu, Yinglong Xia, Hong Li, Hong Yan, Jian Kang
arXiv:2608. 10983v1 Announce Type: cross Abstract: Multi-modal recommenders fuse collaborative signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates.
By Pengyu Zhang, Yangqin Jiang, Klim Zaporojets, Congfeng Cao, Paul Groth
Multi-modal recommenders fuse collaborative signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates. For example, chocolate purchases typically guided by textual ingredient cues can shift toward visual packaging and ambient audio around Valentine's Day.
Positional encoding is a fundamental component of Transformer-based generative recommendation models, where user histories are modeled as autoregressive item sequences. Most positional encoding methods are inherited from natural language processing and mainly represent discrete item order.
arXiv:2608. 16274v1 Announce Type: cross Abstract: Positional encoding is a fundamental component of Transformer-based generative recommendation models, where user histories are modeled as autoregressive item sequences.
By Pengfei Jia, Jingjian Wang, Jingmao Li, Ge Zhang, Feng Shi