Le Wu

DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all …

Joint Item Recommendation and Attribute Inference

2020

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item …

Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning

2022 · Proceedings of the ACM Web Conference 2022

Knowledge graphs (KGs) have been widely used to improve recommendation accuracy. The multi-hop paths on KGs also enable recommendation reasoning, which is considered a crystal type of explainability. In this paper, we propose a reinforcement …

A Neural Influence Diffusion Model for Social Recommendation

2019

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the …

Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction …

Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

2021

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors …

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant …

A Review-aware Graph Contrastive Learning Framework for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing …