7 citations · 12 across the 8 of their papers we have counts for
9 papers
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…
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…
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
Kunal Dahiya, Nilesh Gupta, Deepak Saini +16
Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representation…
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Junhan Yang, Zheng Liu, Bowen Jin +7
Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates t…
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
Jianxun Lian, Iyad Batal, Zheng Liu +4
Precise user modeling is critical for online personalized recommendation services. Generally, users' interests are diverse and are not limited to a single aspect, which is particul…
Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks
Mehul Parsana, Krishna Poola, Yajun Wang +1
Click through rate (CTR) prediction is very important for Native advertisement but also hard as there is no direct query intent. In this paper we propose a large-scale event embedd…