J
Jiaxi Tang
Researcher at Simon Fraser University
Publications - 18
Citations - 1985
Jiaxi Tang is an academic researcher from Simon Fraser University. The author has contributed to research in topics: Recommender system & Ranking. The author has an hindex of 9, co-authored 13 publications receiving 1137 citations. Previous affiliations of Jiaxi Tang include Google.
Papers
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Proceedings ArticleDOI
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Jiaxi Tang,Ke Wang +1 more
TL;DR: A Convolutional Sequence Embedding Recommendation Model »Caser» is proposed, which is to embed a sequence of recent items into an image in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters.
Proceedings ArticleDOI
Sequential Recommendation with User Memory Networks
TL;DR: A memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation is designed, which store and update users» historical records explicitly, which enhances the expressiveness of the model.
Proceedings ArticleDOI
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Jiaxi Tang,Ke Wang +1 more
TL;DR: In this article, a ranking distillation (RD) method was proposed to train a student model to learn to rank documents/items from both the training data and the supervision of a larger teacher model.
Posted Content
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Jiaxi Tang,Ke Wang +1 more
TL;DR: A novel way to train ranking models, such as recommender systems, that are both effective and efficient is proposed, and a smaller student model is trained to learn to rank documents/items from both the training data and the supervision of a larger teacher model.
Posted Content
Understanding and Improving Knowledge Distillation
TL;DR: This paper dissects the effects of knowledge distillation into three main factors: (1) benefits inherited from label smoothing, (2) example re-weighting based on teacher's confidence on ground-truth, and (3) prior knowledge of optimal output (logit) layer geometry.