7 citations · 13 across the 7 of their papers we have counts for
7 papers
Melo: A Production LLM-Powered Music Recommendation Agent
Shijia Wang, Da Guo, Qiang Xiao +4
We describe Melo, an LLM-powered music recommendation agent deployed on NetEase Cloud Music. Melo is structured as a deterministic five-node state graph over heterogeneous tools, w…
PIANO: Personalized Reranking via Information Aggregation Node for Music Search Optimization
Weisheng Li, Chuqiao Huang, Pengcheng Li +5
Unlike short-video content, music tracks have long lifecycles and lasting value. Effective music search re-ranking must therefore align the user's current query with long-term pref…
L2Rec: Towards Dual-View Understanding of LLMs for Personalized Recommendation
Pingjun Pan, Tingting Zhou, Peiyao Lu +3
Adapting large language models (LLMs) for personalized recommendation requires aligning their general-purpose capabilities with user-specific preferences while effectively leveragi…
FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency
Xianwen Guo, Bin Huang, Xiaomeng Wu +6
Generative recommendation (GR) models possess greater scaling power compared to traditional deep learning recommendation models (DLRMs), yet they also impose a tremendous increase…
Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation
Shijia Wang, Tianpei Ouyang, Qiang Xiao +5
In music recommendation systems, multimodal interest learning is pivotal, which allows the model to capture nuanced preferences, including textual elements such as lyrics and vario…
Advancing the Foundation Model for Music Understanding
Yi Jiang, Wei Wang, Xianwen Guo +6
The field of Music Information Retrieval (MIR) is fragmented, with specialized models excelling at isolated tasks. In this work, we challenge this paradigm by introducing a unified…
Climber: Toward Efficient Scaling Laws for Large Recommendation Models
Songpei Xu, Shijia Wang, Da Guo +5
Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent ch…