122 papers with code • 1 benchmarks • 4 datasets
These leaderboards are used to track progress in Sequential Recommendation
LibrariesUse these libraries to find Sequential Recommendation models and implementations
Most implemented papers
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.
Self-Attentive Sequential Recommendation
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently.
TiSASRec: Time Interval Aware Self-Attention for Sequential Recommendation
Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'.
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow.
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
User modeling is an essential task for online rec- ommender systems.
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation
Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry.
Hierarchical Gating Networks for Sequential Recommendation
However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
Sequential patterns play an important role in building modern recommender systems.
SSE-PT: Sequential Recommendation Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with.