Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks

@article{Qiu2020ExploitingCI,
  title={Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks},
  author={Ruihong Qiu and Zi Huang and Jingjing Li and Hongzhi Yin},
  journal={ACM Transactions on Information Systems (TOIS)},
  year={2020},
  volume={38},
  pages={1 - 23}
}
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does… 
A Survey on Session-based Recommender Systems
TLDR
A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.
User Response Prediction in Online Advertising
TLDR
A taxonomy is proposed to categorize state-of-the-art user response prediction methods, primarily focusing on the current progress of machine learning methods used in different online platforms, and applications of user response Prediction, benchmark datasets, and open source codes in the field are reviewed.
A Graph Theoretic Approach for Multi-Objective Budget Constrained Capsule Wardrobe Recommendation
TLDR
The objective is to find a 1-neighbor subset of fashion items as a capsule wardrobe that jointly maximize compatibility and versatility scores by considering corresponding user-specified preference weight coefficients and an overall shopping budget as a means of achieving personalization.
Disentangled Graph Neural Networks for Session-based Recommendation
  • Ansong Li, Zhiyong Cheng, Fan Liu, Zan Gao, Weili Guan, Yuxin Peng
  • Computer Science
  • 2022
TLDR
This work proposes a novel method called Disentangled Graph Neural Network (Disen-GNN) to capture the session purpose with the consideration of factor-level attention on each item, and takes user intents at the factor level into account to infer the user purpose in a session.
Exploiting Positional Information for Session-based Recommendation
TLDR
A novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module is proposed to fully exploit the positional information for session-based recommendation tasks.
Graph Co-Attentive Session-based Recommendation
Session-based recommendation aims to generate recommendations merely based on the ongoing session, which is a challenging task. Previous methods mainly focus on modeling the sequential signals or the
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
TLDR
This work proposes to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and designs a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends' preferences, which provides a solid foundation for learning groups’ preference.
Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach
  • Chao Wang, Hengshu Zhu, +4 authors Hui Xiong
  • ACM Transactions on Information Systems
  • 2022
As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
TLDR
RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation via the filtering threshold mechanism, as opposed to other comparative GNN models.
A Survey on Session-based Recommender Systems
Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent...
...
1
2
3
...

References

SHOWING 1-10 OF 93 REFERENCES
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
TLDR
These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior
Gated Graph Sequence Neural Networks
TLDR
This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.
Session-based Recommendations with Recurrent Neural Networks
TLDR
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem.
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
On Both Cold-Start and Long-Tail Recommendation with Social Data
TLDR
This paper exploits the benefits of jointly challenging both cold-start and long-tail recommendation, and proposes a novel approach which can simultaneously handle both of them in a unified objective and presents an iterative optimization algorithm.
  • 2020
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
TLDR
This paper proposes a novel Sequence-Aware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies.
Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation
TLDR
This work proposes a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation that consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short- term preference learning.
  • 2019
...
1
2
3
4
5
...