Learning a recommender system model from an item's raw modality features...
Recommender systems (RS) have achieved significant success by leveraging...
Adapters, a plug-in neural network module with some tunable parameters, ...
Text-based collaborative filtering (TCF) has become the mainstream appro...
Recommendation models that utilize unique identities (IDs) to represent
...
Existing benchmark datasets for recommender systems (RS) either are crea...
Large-scale Protein Language Models (PLMs) have improved performance in
...
Learning big models and then transfer has become the de facto practice i...
Deep neural networks (DNN) have achieved great success in the recommende...
This ability to learn consecutive tasks without forgetting how to perfor...
Sequential recommender systems (SRS) have become a research hotspot due ...
Lifelong learning capabilities are crucial for sentiment classifiers to
...
Making accurate recommendations for cold-start users has been a longstan...
Deep learning has brought great progress for the sequential recommendati...
Learning generic user representations which can then be applied to other...
Recently, Memory-based Neural Recommenders (MNR) have demonstrated super...
Sequential recommender systems (SRS) have become the key technology in
c...
Inductive transfer learning has had a big impact on computer vision and ...
Inductive transfer learning has greatly impacted the computer vision and...
As the core of recommender system, collaborative filtering (CF) models t...
With the prevalence of multimedia content on the Web, developing recomme...
Convolutional Neural Networks (CNNs) models have been recently introduce...
Jointing visual-semantic embeddings (VSE) have become a research hotpot ...
Although the word-popularity based negative sampler has shown superb
per...