Session-Based Recommendations
57 papers with code • 7 benchmarks • 2 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
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Libraries
Use these libraries to find Session-Based Recommendations models and implementations
Gowalla
Most implemented papers
Xception: Deep Learning with Depthwise Separable Convolutions
tensorflow/models
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CVPR 2017
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).
Session-based Recommendations with Recurrent Neural Networks
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
Session-based Recommendation with Graph Neural Networks
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Neural Attentive Session-based Recommendation
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.
Evaluation of Session-based Recommendation Algorithms
rn5l/session-rec
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In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session.
News Session-Based Recommendations using Deep Neural Networks
gabrielspmoreira/chameleon_recsys
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This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks.
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session.
Session-aware Linear Item-Item Models for Session-based Recommendation
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e. g., on e-commerce or multimedia streaming services.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.
