2 citations · 4 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System
Yongchun Zhu, Jingwu Chen, Ling Chen +4
Feature modeling, which involves feature representation learning and leveraging, plays an essential role in industrial recommendation systems. However, the data distribution in rea…