Tieniu Tan

Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback

2015

Pairwise learning algorithms are a vital technique for personalized ranking with implicit feedback. They usually assume that each user is more interested in items which have been selected by the user than remaining ones. This …

Layer-wise contrastive network for unsupervised graph representation learning

2026 · Neurocomputing

Unsupervised graph representation learning has emerged as a cornerstone for extracting meaningful insights from complex relational data. While contrastive learning paradigms have achieved notable success, they traditionally rely on stochastic data augmentations to generate multiple …

A Dynamic Recurrent Model for Next Basket Recommendation

2016

Next basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users' general interests and sequential behaviors as two totally divided matters, …

Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more …

Session-Based Recommendation with Graph Neural Networks

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising …