Session-Based Recommendations
75 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
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time
Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation.
Neural Att entive Session-based Recommendation
Wang-Shuo/Neural-Attentive-Session-Based-Recommendation-PyTorch
•
•
CIKM 2017
Specifically, we explore a hybrid encoder with an attention mechanism to model the user’s sequential behavior and capture the user’s main purpose in the current session, which are combined as a unified session representation later.
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
In this work we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction.
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.
Empirical Analysis of Session-Based Recommendation Algorithms
rn5l/session-rec
•
•
However, previous research indicates that today's complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy.
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
RuihongQiu/FGNN
•
•
In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.
CHAMELEON: A Deep Learning Meta-Architecture for News Recommender Systems [Phd. Thesis]
gabrielspmoreira/chameleon_recsys
•
•
The main contribution of this research was named CHAMELEON, a Deep Learning meta-architecture designed to tackle the specific challenges of news recommendation.
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
CRIPAC-DIG/TAGNN
•
•
However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
ciecus/MKM-SR
•
•
These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation.
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
RuihongQiu/GAG
•
•
On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.
