Latent mutual feature extraction for cross-domain recommendation
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10115-024-02065-y.pdf
Reference21 articles.
1. Hong M, Jung JJ (2021) ClustPTF: Clustering-based parallel tensor factorization for the diverse multi-criteria recommendation. Electron Commer Res Appl 47(January):101041. https://doi.org/10.1016/j.elerap.2021.101041
2. Yu X, Hu Q, Li H, Du J, Gao J, Sun L (2022) Cross-domain recommendation based on latent factor alignment. Neural Comput Appl 34(5):3421–3432. https://doi.org/10.1007/s00521-021-05737-w
3. Srifi M, Oussous A, Lahcen AA, Mouline S (2020) Recommender systems based on collaborative filtering using review texts-A survey. Information (Switzerland) 11(6):1–21. https://doi.org/10.3390/INFO11060317
4. Wang, Q., Nguyen, Q.V.H., Yin, H., Huang, Z., Wang, H., Cui, L.: Enhancing collaborative filtering with generative augmentation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 548–556 (2019). https://doi.org/10.1145/3292500.3330873
5. Ifada, N., Alim, D.R.M., Sophan, M.K.: NMF-based DCG optimization for collaborative ranking on recommendation systems. ACM International Conference Proceeding Series, 7–11 (2019). https://doi.org/10.1145/3366750.3366753
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