Abstract
Recommender systems have achieved widespread success in real-life applications. Personalized recommendation can reduce customers’ effort in finding items they are interested in. It is also critical in some industries as it can increase customer stickiness and help industries to stand out from competitors. Recommender systems made a significant progress over the last decade, and the advancements are fruitful and inspiring. Given its importance, this chapter aims at introducing the fundamentals and advances of recommender systems. In specific, we will present readers with the widely used techniques, applications, and evaluation methods of recommender systems, in the hope that it could help them to get a thorough and clear understanding to this field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. WWW.
Koren, Yehuda, Robert Bell, and Chris Volinsky. “Matrix factorization techniques for recommender systems.” Computer 8 (2009): 30–37.
Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. KDD. ACM, New York, NY, USA, 426–434.
Ma, Hao, et al. “Recommender systems with social regularization.” WSDM. ACM, 2011.
Mnih, Andriy, and Ruslan R. Salakhutdinov. “Probabilistic matrix factorization.” Advances in Neural Information Processing Systems. 2008.
Rendle, Steffen, et al. “BPR: Bayesian personalized ranking from implicit feedback.” UAI. AUAI Press, 2009.
Hsieh, Cheng-Kang, et al. “Collaborative metric learning.” WWW, 2017.
Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. “Sequence-aware recommender systems.” ACM Computing Surveys (CSUR) 51.4 (2018): 66.
Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. IJCAI.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
Zhang, Shuai, et al. “Deep learning based recommender system: A survey and new perspectives.” ACM Computing Surveys (CSUR) 52.1 (2019): 5.
He, Xiangnan, et al. “Neural collaborative filtering.” WWW, 2017.
Sedhain, Suvash, et al. “Autorec: Autoencoders meet collaborative filtering.” WWW. ACM, 2015.
Wu, Yao, et al. “Collaborative denoising auto-encoders for top-n recommender systems.” WSDM. ACM, 2016.
Cheng, Heng-Tze, et al. “Wide & deep learning for recommender systems.” Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016.
Guo, Huifeng, et al. “DeepFM: a factorization-machine based neural network for CTR prediction.” arXiv preprint arXiv:1703.04247 (2017).
He, Kaiming, et al. “Deep residual learning for image recognition.” CVPR. 2016.
Zhang, Shuai, et al. “Next Item Recommendation with Self-Attentive Metric Learning.” AAAI Conference. Vol. 9. 2019.
Ying, Rex, et al. “Graph convolutional neural networks for web-scale recommender systems.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018.
Kipf, Thomas N., and Max Welling. “Semi-supervised classification with graph convolutional networks.” arXiv preprint arXiv:1609.02907 (2016).
Zheng, Guanjie, et al. “DRN: A deep reinforcement learning framework for news recommendation.” WWW, 2018.
Covington, Paul, Jay Adams, and Emre Sargin. “Deep neural networks for YouTube recommendations.” RecSys. ACM, 2016.
Ricci, Francesco, Lior Rokach, and Bracha Shapira. “Introduction to recommender systems handbook.” Recommender Systems Handbook. Springer, Boston, MA, 2011. 1–35.
Ge, Mouzhi, Carla Delgado-Battenfeld, and Dietmar Jannach. “Beyond accuracy: evaluating recommender systems by coverage and serendipity.” RecSys. ACM, 2010.
Pazzani, Michael J., and Daniel Billsus. “Content-based recommendation systems.” In The adaptive web, pp. 325–341. Springer, Berlin, Heidelberg, 2007.
Burke, R., 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), pp.331–370.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. and Polosukhin, I., 2017. Attention is all you need. arXiv preprint arXiv:1706.03762.
Barkan, O., Koenigstein, N., Yogev, E. and Katz, O., 2019, September. CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations. RecSys (pp. 228–236).
Malkiel, I., Barkan, O., Caciularu, A., Razin, N., Katz, O. and Koenigstein, N., 2020. RecoBERT: A Catalog Language Model for Text-Based Recommendations. arXiv preprint arXiv:2009.13292.
Linden, Greg, Brent Smith, and Jeremy York. “Amazon.com recommendations: Item-to-item collaborative filtering.” IEEE Internet Computing 7, no. 1 (2003): 76–80.
Bennett, J. and Lanning, S., 2007, August. The Netflix Prize. In Proceedings of KDD Cup and Workshop (Vol. 2007, p. 35).
Koenigstein, N., Nice, N., Paquet, U. and Schleyen, N., 2012, September. The Xbox recommender system. In RecSys (pp. 281–284).
Barkan, O. and Koenigstein, N., 2016, September. Item2vec: neural item embedding for collaborative filtering. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6). IEEE.
Mikolov, T., Chen, K., Corrado, G. and Dean, J., 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Barkan, O., Fuchs, Y., Caciularu, A. and Koenigstein, N., 2020, September. Explainable recommendations via attentive multi-persona collaborative filtering. In RecSys (pp. 468–473).
Zhang, Yongfeng, and Xu Chen. “Explainable Recommendation: A Survey and New Perspectives.” Foundations and TrendsÁő in Information Retrieval 14, no. 1 (2020): 1–101.
Kang, W.C., Fang, C., Wang, Z. and McAuley, J., 2017, November. Visually-aware fashion recommendation and design with generative image models. ICDM (pp. 207–216). IEEE.
Phanich, M., Pholkul, P. and Phimoltares, S., 2010, April. Food recommendation system using clustering analysis for diabetic patients. In 2010 International Conference on Information Science and Applications (pp. 1–8). IEEE.
Goldberg, D., Nichols, D., Oki, B.M. and Terry, D., 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), pp. 61–70.
Vinh Tran, L., Tay, Y., Zhang, S., Cong, G. and Li, X., 2020, January. HyperML: a boosting metric learning approach in hyperbolic space for recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 609–617).
Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zhang, S., Zhang, A., Yao, L. (2023). Recommender Systems. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-24628-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24627-2
Online ISBN: 978-3-031-24628-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)