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
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Libraries
Use these libraries to find Session-Based Recommendations models and implementations
Gowalla
OTTO Recommender Systems Dataset
Latest papers
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
otto-de/tron
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This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes
A general SBR uses only information about the item of interest to make a recommendation (e. g., item ID for an EC site).
Self Contrastive Learning for Session-based Recommendation
zhengxiangshi/selfcontrastivelearningrecsys
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Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
ee1s/nirgnn
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We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
Learning Recommendations from User Actions in the Item-poor Insurance Domain
simonebbruun/cross-sessions_rs
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To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations.
STAR: A Session-Based Time-Aware Recommender System
Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them.
Efficient On-Device Session-Based Recommendation
Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
sustechbruce/restc-source-code
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The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.
Understanding Diversity in Session-Based Recommendation
qyin863/understanding-diversity-in-sbrss
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Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets.
