S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks

@article{Choi2022SWalkAA,
  title={S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks},
  author={Minjin Choi and Jinhong Kim and Joo-Yeon Lee and Hyunjung Shim and Jongwuk Lee},
  journal={Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
  year={2022}
}
  • Minjin Choi, Jinhong Kim, Jongwuk Lee
  • Published 4 January 2022
  • Computer Science
  • Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy andโ€ฆย 

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