Abstract
Traditional recommendation systems work with a single rating provided by a user on an item. However, in many domains such as tourism, hotels, etc., a user would love to give rating for every criterion of an item based on his several experiences throughout the journey and at the same time, a user would love to be recommended considering all the features an item. Hence, the single rating recommendation systems are a bit inadequate for recommending items to the user in these situations and especially when user preferences change dynamically specifically on the criteria of the items that are to be recommended. So to tackle these anomalies in user behaviour, we propose a modified version of deep neural collaborative filtering method that is capable of predicting the criterion ratings of the items. To compare the similarity between criteria ratings and user’s dynamic shift in preferences on the criteria of items, we use some standard similarity techniques. The proposed network is designed to learn from user–item interaction data to predict the criterion ratings. To evaluate our proposed approach, we predict the overall rating of an item through using its criterion ratings with artificial neural networks (ANN). The proposed approach is tested on Yahoo! Movies dataset and TripAdvisor dataset. The proposed approach outperformed many of the baseline recommendation system models.
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
Similar content being viewed by others
References
Adomavicius Gediminas, Kwon YoungOk (2007) New recommendation techniques for multicriteria rating systems. IEEE Intell Syst 22(3):48–55
Cacheda Fidel, Carneiro Víctor, Fernández Diego, Formoso Vreixo (2011) Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web (TWEB) 5(1):2
Cao S, Yang N, Liu Z (2017) Online news recommender based on stacked auto-encoder. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS). IEEE, pp 721–726
Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 278–288
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 173–182
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22(1):5–53
Jannach D, Karakaya Z, Gedikli F (2012) Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM conference on electronic commerce. ACM, pp 674–689
Kingma DP, Adam JB (2014) A method for stochastic optimization. arXiv:1412.6980
Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 426–434
Koren Yehuda, Bell Robert, Volinsky Chris (2009) Matrix factorization techniques for recommender systems. Computer 8:30–37
Liu L, Mehandjiev N, Xu D-L (2011) Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the fifth ACM conference on Recommender systems. ACM, pp 77–84
Sahoo Nachiketa, Krishnan Ramayya, Duncan George, Callan Jamie (2012) Research note–the halo effect in multicomponent ratings and its implications for recommender systems: The case of yahoo! movies. Inf Syst Res 23(1):231–246
Sarwar BM, Karypis G, Konstan JA, Riedl J et al (2001) Item-based collaborative ltering recommendation algorithms. Www 1:285–295
Sreepada RS, Patra BK, Hernando A (2017) Multi-criteria recommendations through preference learning. In Proceedings of the fourth ACM IKDD conferences on data sciences. ACM, p 1
Steffen R (2010) Factorization machines. In: IEEE 10th International Conference on Data Mining (ICDM), pp 995–1000
Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: Proceedings of the 1st workshop on deep learning for recommender systems. ACM, pp 11–16
Wang H, Lu Y, Zhai CX (2011) Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 618–626
Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining. ACM, pp 153–162
Zheng Y (2017) Criteria chains: a novel multi-criteria recommendation approach. In: Proceedings of the 22nd international conference on intelligent user interfaces. ACM, pp 29–33
Zheng Y (2019) Utility-based multi-criteria recommender systems. In: Proceedings of the 34th ACM/SIGAPP symposium on applied computing. ACM, pp 2529–2531
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nithin Goud, K., Ramanjaneyulu, Y.V., Sathya Babu, K., Patra, B.K. (2021). User Preference Multi-criteria Recommendations Using Neural Collaborative Filtering Methods. In: Giri, D., Buyya, R., Ponnusamy, S., De, D., Adamatzky, A., Abawajy, J.H. (eds) Proceedings of the Sixth International Conference on Mathematics and Computing. Advances in Intelligent Systems and Computing, vol 1262. Springer, Singapore. https://doi.org/10.1007/978-981-15-8061-1_5
Download citation
DOI: https://doi.org/10.1007/978-981-15-8061-1_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8060-4
Online ISBN: 978-981-15-8061-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)