References
- N. Alghamdi and F. Assiri, "Solving the cold-start problem in recommender systems using contextual information in arabic from calendars," Arabian Journal for Science and Engineering, pp. 1-9, 2020.
- F. Ricci, L. Rokach, and B. Shapira, "Introduction to recommender systems handbook," in Recommender systems handbook. Springer, 2011, pp. 1-35.
- A. B. Soliman, K. Eissa, and S. R. El-Beltagy, "Aravec: A set of arabic word embedding models for use in arabic nlp," Procedia Computer Science, vol. 117, pp. 256-265, 2017. https://doi.org/10.1016/j.procs.2017.10.117
- A. A. Altowayan and L. Tao, "Word embeddings for arabic sentiment analysis," in 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016, pp. 3820-3825.
- A. Dahou, S. Xiong, J. Zhou, M. H. Haddoud, and P. Duan, "Word embeddings and convolutional neural network for arabic sentiment classification," in Proceedings of coling 2016, the 26th international conference on computational linguistics: Technical papers, 2016, pp. 2418-2427.
- P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," Transactions of the Association for Computational Linguistics, vol. 5, pp. 135-146, 2017. https://doi.org/10.1162/tacl_a_00051
- G. G. Chowdhury, "Natural language processing," Annual review of information science and technology, vol. 37, no. 1, pp. 51-89, 2003. https://doi.org/10.1002/aris.1440370103
- T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," arXiv preprint arXiv:1310.4546, 2013.
- P. Rodriguez Bertorello, "Recommendation engine: Semantic cold start," Available at SSRN 3655839, 2020.
- F. Anwar, N. Iltaf, H. Afzal, and H. Abbas, "A deep learning framework to predict rating for cold start item using item metadata," in 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2019, pp. 313-319.
- Y. C. Yoon and J. W. Lee, "Movie recommendation using metadata based word2vec algorithm," in 2018 International Conference on Platform Technology and Service (PlatCon). IEEE, 2018, pp. 1-6.
- F. Anwaar, N. Iltaf, H. Afzal, and R. Nawaz, "Hrs-ce: A hybrid framework to integrate content embeddings in recommender systems for cold start items," Journal of computational science, vol. 29, pp. 9-18, 2018. https://doi.org/10.1016/j.jocs.2018.09.008
- H. Wang, D. Amagata, T. Makeawa, T. Hara, N. Hao, K. Yonekawa, and M. Kurokawa, "A dnn-based cross-domain recommender system for alleviating cold-start problem in e-commerce," IEEE Open Journal of the Industrial Electronics Society, vol. 1, pp. 194-206, 2020. https://doi.org/10.1109/ojies.2020.3012627
- J. Yuan, W. Shalaby, M. Korayem, D. Lin, K. AlJadda, and J. Luo, "Solving cold-start problem in large-scale recommendation engines: A deep learning approach," in 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016, pp. 1901-1910.
- X. Wang, K. Liu, and J. Zhao, "Handling cold-start problem in review spam detection by jointly embedding texts and behaviors," in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017, pp. 366-376.
- H. Y. Erdin and A. Guran, "Semi-supervised turkish text categorization with word2vec, doc2vec and fasttext algorithms," in 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE, 2019, pp. 1-4.
- H. Kang and J. Yang, "Performance comparison of word2vec and fasttext embedding models," (J. DCS), vol. 21, no. 7, pp. 1335-1343, 2020. https://doi.org/10.9728/dcs.2020.21.7.1335
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
- S. Raschka, Python Machine Learning. Packt Publishing, 2015.
- D. L. Olson and D. Delen, Advanced data mining techniques. Springer Science & Business Media, 2008.
- Y. Sasaki et al., "The truth of the f-measure. 2007," URL: https://www.cs.odu.edu/~mukka/cs795sum09dm/Lecturenotes/Day3/F-measure-YS-26Oct07.pdf [accessed 2021-05-26], 2007.