The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
Open accessAbstract
Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings.
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Cites
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
A Singular Value Thresholding Algorithm for Matrix Completion
Cai, J.-F., Candès, E. J., & Shen, Z. (2010) · SIAM Journal on Optimization · doi:10.1137/080738970 · Cited by 5,809
Probabilistic Matrix Factorization
Mnih, A., & Salakhutdinov, R. (2007) · neural information processing systems · Cited by 3,798
A Survey of Collaborative Filtering Techniques
Su, X., & Khoshgoftaar, T. M. (2009) · Advances in Artificial Intelligence · doi:10.1155/2009/421425 · Cited by 3,602
Hidden factors and hidden topics: understanding rating dimensions with review text
McAuley, J., & Leskovec, J. (2013) · Proceedings of the 7th ACM conference on Recommender systems · doi:10.1145/2507157.2507163 · Cited by 1,564
Collaborative topic modeling for recommending scientific articles
Wang, C., & Blei, D. M. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020480 · Cited by 1,527
Self-citationBayesian probabilistic matrix factorization using Markov chain Monte Carlo
Salakhutdinov, R., & Mnih, A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390267 · Cited by 1,413
SoRec: social recommendation using probabilistic matrix factorization
Ma, H., Yang, H., Lyu, M. R., & King, I. (2008) · Proceedings of the 17th ACM conference on Information and knowledge management · doi:10.1145/1458082.1458205 · Cited by 1,401
Collaborative filtering with temporal dynamics
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Cited by
Hierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1174 · Cited by 4,712
Self-citationDocument Modeling with Gated Recurrent Neural Network for Sentiment Classification
Tang, D., Qin, B., & Liu, T. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1167 · Cited by 1,519
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
Zheng, L., Noroozi, V., & Yu, P. S. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018665 · Cited by 1,075
Knowledge Graph Convolutional Networks for Recommender Systems
Wang, H., Zhao, M., Xie, X., Li, W., & Guo, M. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313417 · Cited by 1,021
Serrano, S., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1282 · Cited by 690
Metadata
- Authors
- Diao, Q., Qiu, M., Wu, C.-Y., Smola, A. J., Jiang, J., & Wang, C.
- Venue
- Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 193-202
- OpenAlex
- W2142972908
- Cited by
- 447
- References
- 25
- FWCI
- 32.20
- Type
- conference paper
- Language
- en
- Open access
- green
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- Diao, Q., Qiu, M., Wu, C.-Y., Smola, A. J., Jiang, J., & Wang, C. (2014). Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2623330.2623758
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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.
