165 citations · 476 across the 9 of their papers we have counts for
9 papers
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Qingquan Song, Dehua Cheng, Hanning Zhou +3
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural ar…
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Yuan Zhang, Xiaoran Xu, Hanning Zhou +1
Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and fl…
Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs
Xiaoran Xu, Songpeng Zu, Yuan Zhang +2
In real-world scenarios, it is appealing to learn a model carrying out stochastic operations internally, known as stochastic computation graphs (SCGs), rather than learning a deter…
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Mengyi Liu, Xiaohui Xie, Hanning Zhou
Video relevance prediction is one of the most important tasks for online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized…
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
Laming Chen, Guoxin Zhang, Hanning Zhou
The determinantal point process (DPP) is an elegant probabilistic model of repulsion with applications in various machine learning tasks including summarization and search. However…
Collaborative Filtering with User-Item Co-Autoregressive Models
Chao Du, Chongxuan Li, Yin Zheng +2
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the unde…