Hongzhi Yin

Utility Mining Across Multi-Dimensional Sequences

2019 · arXiv (Cornell University)

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented …

AIR: Attentional Intention-Aware Recommender Systems

2019

The capability of extracting sequential patterns from the user-item interaction data is now becoming a key feature of recommender systems. Though it is important to capture the sequential effect, existing methods only focus on modelling …

Enhancing Collaborative Filtering with Generative Augmentation

2019

Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply for users with rare interaction data. Most existing hybrid CF methods try to …

Interpretable Signed Link Prediction With Signed Infomax Hyperbolic Graph

2021 · IEEE Transactions on Knowledge and Data Engineering

Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning …

Temporal Meta-path Guided Explainable Recommendation

2021

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendation only utilizes static knowledge graph and ignores the dynamic user-item evolutions, …

Privacy Protection in Deep Multi-modal Retrieval

2021

Deep learning techniques have ushered in significant progress in large-scale multi-modal retrieval. Nevertheless, the advanced techniques may be used nefariously to conduct a search that violates the privacy of individuals. In this paper, we propose …

Graph Embedding for Recommendation against Attribute Inference Attacks

2021

In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be naturally modelled as graph-structured data, variants of graph convolutional …

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. …

Automated Similarity Metric Generation for Recommendation

2024 · arXiv (Cornell University)

The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined similarity metrics, such as the inner product, to calculate similarity …

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

2024

In Location-based Social Networks (LBSNs), Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the conventional cloud-based model to on-device recommendations for privacy protection and reduced server reliance. Due …

Adapting to User Interest Drift for POI Recommendation

2016 · IEEE Transactions on Knowledge and Data Engineering

Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical …

Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation

2016 · ACM Transactions on Information Systems

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. …

Learning Graph-based POI Embedding for Location-based Recommendation

2016

With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), location-based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs). …

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

2017 · IEEE Transactions on Knowledge and Data Engineering

Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder …

Joint Event-Partner Recommendation in Event-Based Social Networks

2018

With the prevalent trend of combining online and offline interactions among users in event-based social networks (EBSNs), event recommendation has become an essential means to help people discover new interesting events to attend. However, existing …

Streaming Ranking Based Recommender Systems

2018

Studying recommender systems under streaming scenarios has become increasingly important because real-world applications produce data continuously and rapidly. However, most existing recommender systems today are designed in the context of an offline setting. Compared with …

Neural Memory Streaming Recommender Networks with Adversarial Training

2018

With the increasing popularity of various social media and E-commerce platforms, large volumes of user behaviour data (e.g., user transaction data, rating and review data) are being continually generated at unprecedented and ever-increasing scales. It …

Social Influence-Based Group Representation Learning for Group Recommendation

2019

As social animals, attending group activities is an indispensable part in people's daily social life, and it is an important task for recommender systems to suggest satisfying activities to a group of users. The major …

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long …

Enhancing Social Recommendation With Adversarial Graph Convolutional Networks

2020 · IEEE Transactions on Knowledge and Data Engineering

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According …

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Recent advancements of sequential deep learning models such as Transformer and BERT have significantly facilitated the sequential recommendation. However, according to our study, the distribution of item embeddings generated by these models tends to degenerate …

Are Graph Augmentations Necessary?

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

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling …