56 citations · 59 across the 4 of their papers we have counts for
4 papers
Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Xiao Lin, Zhicheng Tang, Weilin Cong +14
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long…
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Mengyue Hang, Tobias Schnabel, Longqi Yang +1
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i.e., users and items) is available upfront at training time.…
A Collective Learning Framework to Boost GNN Expressiveness
Mengyue Hang, Jennifer Neville, Bruno Ribeiro
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighbor…
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
Mengyue Hang, Ian Pytlarz, Jennifer Neville
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively st…