13 papers · 1 filter
LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising
Wen Zhang, Xiao Bai, Hsiang-wei Chao +9
Dynamic Product Advertising (DPA) retrieval systems are a core component of modern e-commerce advertising; however, their evaluation remains challenging. Conventional offline engag…
SetMIR: Multi-Interest Retrieval as Set Prediction
Xiaodong Liu, Congfei Zhang, Hsiang-wei Chao +13
Embedding-based retrieval is at the core of industrial recommender systems, but a single user embedding is often too limited to capture a user's diverse interests. Multi-interest r…
EGR: Embedding-Native Generative Retrieval with a Shared LLM
Xiaodong Liu, Congfei Zhang, Hsiang-wei Chao +13
Generative retrieval is increasingly popular in large-scale recommendation and advertising systems, yet current methods introduce practical complications. Semantic-ID methods rely…
Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation
Jingzhe Liu, Hanbing Wang, Jiliang Tang +4
Generative recommendation (GR) is an increasingly popular paradigm in recommender systems, with a prominent line of work using LLMs as autoregressive backbones to predict the next…
OneRetrieval: Unifying Multi-Branch E-commerce Retrieval with an Editable Generative Model
Xuxin Zhang, Ben Chen, Yue Lv +13
Industrial e-commerce search serves hundreds of millions of items through a multi-branch retrieval stage fused by hand-tuned merging without joint optimization. Generative retrieva…
RAG: Retriever Routing for Retrieval-Augmented Generation
Tong Zhao, Yutao Zhu, Yucheng Tian +1
Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the ``one-size-fits-all'' retriev…
Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices
Clark Mingxuan Ju, Tong Zhao, Leonardo Neves +15
Effective item identifiers (IDs) are an important component for recommender systems (RecSys) in practice, and are commonly adopted in many use cases such as retrieval and ranking.…
OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework
Ben Chen, Siyuan Wang, Yufei Ma +20
Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems. Compared to multi-stage cascaded architecture, it offers advantages such as end-to-end join…
MemRec: Collaborative Memory-Augmented Agentic Recommender System
Weixin Chen, Yuhan Zhao, Jingyuan Huang +6
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predic…
Generative Recommendation with Semantic IDs: A Practitioner's Handbook
Clark Mingxuan Ju, Liam Collins, Leonardo Neves +4
Generative recommendation (GR) has gained increasing attention for its promising performance compared to traditional models. A key factor contributing to the success of GR is the s…
Revisiting Self-attention for Cross-domain Sequential Recommendation
Clark Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar +7
Sequential recommendation is a popular paradigm in modern recommender systems. In particular, one challenging problem in this space is cross-domain sequential recommendation (CDSR)…
Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
Clark Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar +11
The development of powerful user representations is a key factor in the success of recommender systems (RecSys). Online platforms employ a range of RecSys techniques to personalize…
Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems
Qiyao Ma, Menglin Yang, Mingxuan Ju +3
Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balance content exploration and exploitatio…