Temporal-Contextual Recommendation in Real-Time

@article{Ma2020TemporalContextualRI,
  title={Temporal-Contextual Recommendation in Real-Time},
  author={Y. Ma and B. Narayanaswamy and Haibin Lin and Hao Ding},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Y. Ma, B. Narayanaswamy, +1 author Hao Ding
  • Published 2020
  • Computer Science
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Personalized real-time recommendation has had a profound impact on retail, media, entertainment and other industries. However, developing recommender systems for every use case is costly, time consuming and resource-intensive. To fill this gap, we present a black-box recommender system that can adapt to a diverse set of scenarios without the need for manual tuning. We build on techniques that go beyond simple matrix factorization to incorporate important new sources of information: the temporal… Expand
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