The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Pairwise interaction tensor factorization for personalized tag recommendation
Abstract
Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.
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Cites
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BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
Self-citationA Multilinear Singular Value Decomposition
De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479896305696 · Cited by 4,106
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401944 · Cited by 3,852
Some Mathematical Notes on Three-Mode Factor Analysis
Tucker, L. R. (1966) · Psychometrika · doi:10.1007/bf02289464 · Cited by 3,492
Collaborative Filtering for Implicit Feedback Datasets
Hu, Y., Koren, Y., & Volinsky, C. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.22 · Cited by 3,170
Harshman, R. A. (1970) · Cited by 2,255
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16
Storkey, A. (2004) · neural information processing systems · Cited by 2,085
Latent semantic models for collaborative filtering
Hofmann, T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963774 · Cited by 1,378
Collaborative filtering with temporal dynamics
Koren, Y. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557072 · Cited by 1,203
Cited by
Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189
Self-citationFactorizing personalized Markov chains for next-basket recommendation
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772773 · Cited by 2,692
Self-citationA Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,638
Deriving Validity Time in Knowledge Graph
Leblay, J., & Chekol, M. W. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3191639 · Cited by 365
Improving pairwise learning for item recommendation from implicit feedback
Rendle, S., & Freudenthaler, C. (2014) · Proceedings of the 7th ACM international conference on Web search and data mining · doi:10.1145/2556195.2556248 · Cited by 360
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Metadata
- Authors
- Rendle, S., & Schmidt-Thieme, L.
- Venue
- Proceedings of the third ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2010-02-04
- Pages
- 81-90
- OpenAlex
- W2089349245
- Cited by
- 675
- References
- 23
- FWCI
- 27.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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