Session-Based Recommendations
74 papers with code • 7 benchmarks • 3 datasets
Recommendation based on a sequence of events. e.g. next item prediction
Benchmarks
These leaderboards are used to track progress in Session-Based Recommendations
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Session-Based Recommendations models and implementations
Gowalla
OTTO Recommender Systems Dataset
Most implemented papers
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
gabrielspmoreira/chameleon_recsys
•
•
The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article.
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
gabrielspmoreira/chameleon_recsys
•
•
A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, a setting named session-based recommendation.
NISER: Normalized Item and Session Representations to Handle Popularity Bias
The models using normalized item and session-graph representations perform significantly better: i. for the less popular long-tail items in the offline setting, and ii.
Modeling Personalized Item Frequency Information for Next-basket Recommendation
HaojiHu/TIFUKNN
•
•
NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.
An efficient manifold density estimator for all recommendation systems
Synerise/booking-challenge
•
•
Many unsupervised representation learning methods belong to the class of similarity learning models.
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
xiaxin1998/DHCN
•
•
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
CCIIPLab/GCE-GNN
•
•
In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph.
Self-Supervised Graph Co-Training for Session-based Recommendation
In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation.
Inter-Session Modeling for Session-Based Recommendation
In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems.
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user state at recommendation time.
