116 citations · 267 across the 9 of their papers we have counts for
3 papers · 1 filter
MURAL: Multimodal, Multitask Retrieval Across Languages
Aashi Jain, Mandy Guo, Krishna Srinivasan +5
Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal…
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…
Joint Text Embedding for Personalized Content-based Recommendation
Ting Chen, Liangjie Hong, Yue Shi +1
Learning a good representation of text is key to many recommendation applications. Examples include news recommendation where texts to be recommended are constantly published every…