437 citations · 721 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation
Wang-Cheng Kang, Julian McAuley
Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale. Candidate generation and re-ranking based approaches are often adopted in…
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
An Yan, Shuo Cheng, Wang-Cheng Kang +2
Sequential patterns play an important role in building modern recommender systems. To this end, several recommender systems have been built on top of Markov Chains and Recurrent Mo…
Learning Consumer and Producer Embeddings for User-Generated Content Recommendation
Wang-Cheng Kang, Julian McAuley
User-Generated Content (UGC) is at the core of web applications where users can both produce and consume content. This differs from traditional e-Commerce domains where content pro…
Recommendation Through Mixtures of Heterogeneous Item Relationships
Wang-Cheng Kang, Mengting Wan, Julian McAuley
Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data. Specific instances make use of signals ranging from user f…
Self-Attentive Sequential Recommendation
Wang-Cheng Kang, Julian McAuley
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recen…
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…