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140 papers · 1 filter
Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention
Rengan Xu, Junjie Yang, Yifan Xu +17
The integration of hardware accelerators has significantly advanced the capabilities of modern recommendation systems, enabling the exploration of complex ranking paradigms previou…
Revisiting Neural Retrieval on Accelerators
Jiaqi Zhai, Zhaojie Gong, Yueming Wang +4
Retrieval finds a small number of relevant candidates from a large corpus for information retrieval and recommendation applications. A key component of retrieval is to model (user,…
Active Learning for Non-Parametric Choice Models
Fransisca Susan, Negin Golrezaei, Ehsan Emamjomeh-Zadeh +1
We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be ident…
On the duality between contrastive and non-contrastive self-supervised learning
Quentin Garrido, Yubei Chen, Adrien Bardes +2
Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and…
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Jian Kang, Yan Zhu, Yinglong Xia +2
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respec…
On Sampling Collaborative Filtering Datasets
Noveen Sachdeva, Carole-Jean Wu, Julian McAuley
We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated…