567 citations · 646 across the 17 of their papers we have counts for
7 papers · 1 filter
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao +3
Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we…
Learning to Embed Categorical Features without Embedding Tables for Recommendation
Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao +4
Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. T…
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Zhe Chen, Yuyan Wang, Dong Lin +4
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification an…
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Ruoxi Wang, Rakesh Shivanna, Derek Z. Cheng +4
Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective cros…
Content-based Music Similarity with Triplet Networks
Joseph Cleveland, Derek Cheng, Michael Zhou +2
We explore the feasibility of using triplet neural networks to embed songs based on content-based music similarity. Our network is trained using triplets of songs such that two son…
Self-supervised Learning for Large-scale Item Recommendations
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng +8
Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with…