3 citations · 7 across the 7 of their papers we have counts for
7 papers
MERGE: Next-Generation Item Indexing Paradigm for Large-Scale Streaming Recommendation
Jing Yan, Yimeng Bai, Zongyu Liu +7
Item indexing, which maps a large corpus of items into compact discrete representations, is critical for both discriminative and generative recommender systems, yet existing Vector…
Brownian Bridge Diffusion for Sequential Recommendation
Yimeng Bai, Yang Zhang, Sihao Ding +5
Diffusion models, known for their strong generative capability derived from iterative noising and denoising processes, have recently emerged as a promising paradigm for sequential…
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
Haoxuan Li, Chunyuan Zheng, Sihao Ding +4
Selection bias in recommender system arises from the recommendation process of system filtering and the interactive process of user selection. Many previous studies have focused on…
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
Jinqiu Jin, Sihao Ding, Wenjie Wang +1
Common click-through rate (CTR) prediction recommender models tend to exhibit feature-level bias, which leads to unfair recommendations among item groups and inaccurate recommendat…
Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance
Gao Xiangyu, Ding Sihao, Dasari Harshavardhan Reddy
Inferring the drivable area in a scene is crucial for ensuring a vehicle avoids obstacles and facilitates safe autonomous driving. In this paper, we concentrate on detecting the in…
Causal Incremental Graph Convolution for Recommender System Retraining
Sihao Ding, Fuli Feng, Xiangnan He +3
Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based…
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series
Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis +1
Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models.…