4 papers · 1 filter
Enhancing Item Tokenization for Generative Recommendation through Self-Improvement
Runjin Chen, Mingxuan Ju, Ngoc Bui +7
Generative recommendation systems, driven by large language models (LLMs), present an innovative approach to predicting user preferences by modeling items as token sequences and ge…
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
Xinyi Wu, Donald Loveland, Runjin Chen +7
Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constrain…
Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems
Qiyao Ma, Menglin Yang, Mingxuan Ju +3
Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balance content exploration and exploitatio…
Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems
Matthew Kolodner, Mingxuan Ju, Zihao Fan +5
Improving recommendation systems (RS) can greatly enhance the user experience across many domains, such as social media. Many RS utilize embedding-based retrieval (EBR) approaches…