75 citations · 157 across the 21 of their papers we have counts for
9 papers · 1 filter
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
Chang Meng, Ziqi Zhao, Wei Guo +6
Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-fac…
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Yankai Chen, Menglin Yang, Yingxue Zhang +4
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently…
Dual Graph enhanced Embedding Neural Network for CTR Prediction
Wei Guo, Rong Su, Renhao Tan +5
CTR prediction, which aims to estimate the probability that a user will click an item, plays a crucial role in online advertising and recommender system. Feature interaction modeli…
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the abi…
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. A…
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Yishi Xu, Yingxue Zhang, Wei Guo +3
Given the convenience of collecting information through online services, recommender systems now consume large scale data and play a more important role in improving user experienc…
Multi-Graph Convolution Collaborative Filtering
Jianing Sun, Yingxue Zhang, Chen Ma +4
Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users an…
Memory Augmented Graph Neural Networks for Sequential Recommendation
Chen Ma, Liheng Ma, Yingxue Zhang +3
The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may de…
Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence
Chen Ma, Yingxue Zhang, Qinglong Wang +1
The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation service…