Determinantal Point Process Likelihoods for Sequential Recommendation

@article{Liu2022DeterminantalPP,
  title={Determinantal Point Process Likelihoods for Sequential Recommendation},
  author={Yuli Liu and Christian J. Walder and Lexing Xie},
  journal={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2022}
}
  • Yuli LiuChristian J. WalderLexing Xie
  • Published 25 April 2022
  • Computer Science
  • Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the training process of recommender systems, the loss function plays an essential role in guiding the optimization of recommendation models to generate accurate suggestions for users. However, most existing sequential recommendation tech- niques focus on designing algorithms… 
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