75 citations · 124 across the 2 of their papers we have counts for
5 papers
Revisiting Adversarially Learned Injection Attacks Against Recommender Systems
Jiaxi Tang, Hongyi Wen, Ke Wang
Recommender systems play an important role in modern information and e-commerce applications. While increasing research is dedicated to improving the relevance and diversity of the…
Understanding and Improving Knowledge Distillation
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao +4
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, wher…
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Jiaxi Tang, Francois Belletti, Sagar Jain +4
Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and ite…
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Jiaxi Tang, Ke Wang
We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image…
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Jiaxi Tang, Ke Wang
Top- sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top- ranked items that a user will likely interact in a `nea…