most citedPyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation

2 citations · 4 across the 12 of their papers we have counts for

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cs.IR2026

MuChator: Enabling Active Music Discovery via Conversational Music LLMs in Douyin Music

Jiahao Liang, Linzhi Huang, Xuannan Liu +6

Douyin Music, a large-scale platform with millions of daily users, adopts an immersive, feed-based discovery paradigm, where users passively explore music through continuous recomm…

cs.IR2026

ALPBench: A Benchmark for Attribution-level Long-term Personal Behavior Understanding

Lu Ren, Junda She, Xinchen Luo +23

Recent advances in large language models have highlighted their potential for personalized recommendation, where accurately capturing user preferences remains a key challenge. Leve…

cs.IR20251 cited

Asymmetric Diffusion Recommendation Model

Yongchun Zhu, Guanyu Jiang, Jingwu Chen +3

Recently, motivated by the outstanding achievements of diffusion models, the diffusion process has been employed to strengthen representation learning in recommendation systems. Mo…

cs.IR20251 cited

RankMixer: Scaling Up Ranking Models in Industrial Recommenders

Jie Zhu, Zhifang Fan, Xiaoxie Zhu +18

Recent progress on large language models (LLMs) has spurred interest in scaling up recommendation systems, yet two practical obstacles remain. First, training and serving cost on i…

cs.IR20252 cited

Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation

Zhen Gong, Zhifang Fan, Hui Lu +7

Sequential recommendation, a critical task in recommendation systems, predicts the next user action based on the understanding of the user's historical behaviors. Conventional stud…

cs.IR2025

Next-User Retrieval: Enhancing Cold-Start Recommendations via Generative Next-User Modeling

Yu-Ting Lan, Yang Huo, Yi Shen +2

The item cold-start problem is critical for online recommendation systems, as the success of this phase determines whether high-quality new items can transition to popular ones, re…