2 papers
cs.IR2024
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…
cs.IR2024
Improving Out-of-Vocabulary Handling in Recommendation Systems
William Shiao, Mingxuan Ju, Zhichun Guo +5
Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions…