2.9k citations · 5.1k across the 15 of their papers we have counts for
11 papers · 1 filter
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
Zhengyang Su, Isay Katsman, Yueqi Wang +10
Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit from restricting the output space to a c…
PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong +20
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit signi…
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen +3
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods pract…
SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation
Chenwei Cai, Ruining He, Julian McAuley
Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in…
Translation-based Recommendation
Ruining He, Wang-Cheng Kang, Julian McAuley
Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is p…
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
Ruining He, Julian McAuley
Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place t…
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Ruining He, Chen Fang, Zhaowen Wang +1
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand on…
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Ruining He, Chunbin Lin, Jianguo Wang +1
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains li…
Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items
Ruining He, Chunbin Lin, Julian McAuley
To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alter…
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
Ruining He, Charles Packer, Julian McAuley
Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compati…
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Ruining He, Julian McAuley
Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them…