Jiankai Sun

Learning An End-to-End Structure for Retrieval in Large-Scale Recommendations

2021

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, …

Semi-supervised Embedding in Attributed Networks with Outliers

2017 · arXiv (Cornell University)

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of …

Learning to Recommend Accurate and Diverse Items

2017

In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of …