567 citations · 646 across the 17 of their papers we have counts for
8 papers · 1 filter
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
Junpeng Hou, XianXing Zhang, Sai Xiao +6
Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content. To support this journey, w…
ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
Yupeng Hou, Jianmo Ni, Zhankui He +5
Generative recommendation (GR) is an emerging paradigm where user actions are tokenized into discrete token patterns and autoregressively generated as predictions. However, existin…
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi +6
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can parti…
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta +4
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can compre…
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
Yin Zhang, Ruoxi Wang, Tiansheng Yao +5
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts…
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao +3
Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we…
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Ruoxi Wang, Rakesh Shivanna, Derek Z. Cheng +4
Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective cros…
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen +4
Recommender system models often represent various sparse features like users, items, and categorical features via embeddings. A standard approach is to map each unique feature valu…