Abstract
The use of online services such as e-commerce is rapidly growing, resulting in an exponential rise in the amount of information that is available. Recommender systems assist consumers in selecting relevant information from a vast pool of alternatives. Furthermore, varied recommendation contexts have their own challenges, necessitating the use of diverse recommendation approaches. Collaborative filtering techniques have been predominantly used owing to their high accuracy, simplistic approach, and low computational needs, whereas content-based filtering faces the problem of high computational needs. This chapter aims to capture the behaviour of users using a modified Proximity Impact Popularity (Modified-PIP) technique augmented with Deep Neural Network (DNN) to make efficient movie recommendations. The dominance of Modified-PIP over other similarity measures such as PIP similarity, Cosine similarity, and PCC (Pearson Correlation Coefficient) is tested on the MovieLens-100K, MovieLens-1M, and Jester datasets. A matching imputation technique is applied to reduce the sparsity of the dataset. The performance of the proposed Deep Neural Network (DNN) architecture in conjunction with the Modified-PIP similarity measure is evaluated using RMSE (root mean squared error), MAE (mean absolute error), R (recall), and P (precision). Low error scores obtained by the proposed model give justification for the high efficacy of the proposed model.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Supp. Sys. 74, 12–32 (2015)
Bag, S., Kumar, S.K., Tiwari, M.K.: An efficient recommendation generation using relevant Jaccard similarity. Inf. Sci. 483, 53–64 (2019)
Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst. 56, 156–166 (2014)
Shi, X., Luo, X., Shang, M., Gu, L.: Long-term performance of collaborative filtering based recommenders in temporally evolving systems. Neurocomputing 267, 635–643 (2017)
Bellogín, A., Sánchez, P.: Collaborative filtering based on subsequence matching: a new approach. Inf. Sci. 418, 432–446 (2017)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Sinha, B.B., Dhanalakshmi, R.: Evolution of recommender paradigm optimization over time. J. King Saud Univ.-Comput. Inf. Sci. (2019)
Luo, X., Xia, Y., Zhu, Q.: Incremental collaborative filtering recommender based on regularized matrix factorization. Knowl.-Based Syst. 27, 271–280 (2012)
Bobadilla, J., Serradilla, F.: The effect of sparsity on collaborative filtering metrics. In: Proceedings of the Twentieth Australasian Conference on Australasian Database, vol. 92, pp. 9–18, Jan. 2009
Dhanalakshmi, R., Sinha, B.B.: Hybrid Cohort Rating Prediction Technique to leverage Recommender System (2019)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798, June 2007
Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1245–1254, Aug. 2017
Sedhain, S., Menon, A. K., Sanner, S., Xie, L.: Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pp. 111–112, May 2015
Strub, F., Mary, J.: Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: NIPS Workshop on Machine Learning for eCommerce, Dec. 2015
Singh, P.K., Sinha, M., Das, S., Choudhury, P.: Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item. Appl. Intell. 50(12), 4708–4731 (2020)
Wu, Y., DuBois, C., Zheng, A. X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 153–162, Feb. 2016
Yu, J.B.: Evolutionary manifold regularized stacked denoising autoencoders for gearbox fault diagnosis. Knowl.-Based Syst. 178, 111–122 (2019)
Sinha, B.B., Dhanalakshmi, R.: DNN-MF: deep neural network matrix factorization approach for filtering information in multi-criteria recommender systems. Neural Comput. Appl. 1–15 (2022)
Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering versus matrix factorization revisited. In: Fourteenth ACM Conference on Recommender Systems, pp. 240–248, Sept. 2020
Tan, Z., He, L.: An efficient similarity measure for user-based collaborative filtering recommender systems inspired by the physical resonance principle. IEEE Access 5, 27211–27228 (2017)
Guo, G., Zhang, J., Yorke-Smith, N.: A novel bayesian similarity measure for recommender systems. In: Twenty-third International Joint Conference on Artificial Intelligence, June 2013
Abualigah, L.M., Khader, A.T.: Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J. Supercomput. 73(11), 4773–4795 (2017)
Abualigah, L.M.Q., Hanandeh, E.S.: Applying genetic algorithms to information retrieval using vector space model. Int. J. Comput. Sci. Eng. Appl. 5(1), 19 (2015)
Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178(1), 37–51 (2008)
Ahire, J.B.: The artificial neural networks handbook: Part 4. Medium (2018). https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Sinha, B.B., Dhanalakshmi, R.: Building a fuzzy logic-based artificial neural network to uplift recommendation accuracy. Comput. J. 63(11), 1624–1632 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sinha, B.B., Yadav, G.S., Kudkelwar, S.B. (2022). Modified-PIP with Deep Neural Network (DNN) Architecture: A Coherent Recommendation Framework for Capturing User Behaviour. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-10869-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10868-6
Online ISBN: 978-3-031-10869-3
eBook Packages: Computer ScienceComputer Science (R0)