The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Open accessAbstract
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2021068 · Cited by 346
Cited by
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3357895 · Cited by 2,558
STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation
Liu, Q., Zeng, Y., Mokhosi, R., & Zhang, H. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219950 · Cited by 960
S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411954 · Cited by 789
Evaluation of session-based recommendation algorithms
Ludewig, M., & Jannach, D. (2018) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-018-9209-6 · Cited by 336
Hierarchical Gating Networks for Sequential Recommendation
Ma, C., Kang, P., & Liu, X. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330984 · Cited by 322
Metadata
- Authors
- Quadrana, M., Karatzoglou, A., Hidasi, B., & Cremonesi, P.
- Venue
- Proceedings of the Eleventh ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2017-08-27
- Pages
- 130-137
- OpenAlex
- W2625746539
- Cited by
- 591
- References
- 4
- FWCI
- 28.80
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. https://app.cochat.ai/papers/10.1145/3109859.3109896
- The paper
- Quadrana, M., Karatzoglou, A., Hidasi, B., & Cremonesi, P. (2017). Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. Proceedings of the Eleventh ACM Conference on Recommender Systems. https://doi.org/10.1145/3109859.3109896
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
