5 papers
CAMIE: Co-Engagement-Aware Multimodal Item Embeddings for Snap Dynamic Product Ads Retrieval
Xiaodong Liu, Siman Wang, Congfei Zhang +9
Item-to-item (I2I) retrieval is a core primitive in large-scale recommendation and advertising systems. In production Snap Dynamic Product Ads (DPA), I2I retrieval faces two challe…
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…
SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads
Congfei Zhang, Jingxiao Ma, Xiaodong Liu +14
Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and…
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…
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.…