Sage Journals: Discover world-class research

Abstract

Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers’ preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates the agglomerative matrix for the recommendation using the review data. The customer series matrix, customer series binary matrix, product series matrix, and product series binary matrix make up the agglomerative matrix. The product grouping is carried out to group the similar products using DEC for retrieving the optimal product. Moreover, the bi-level matching generates the best group customer sequence in which the relevant customers are retrieved using tversky index and angular distance. Also, the final product suggestion is made using matrix factorization, with the goal of recommending to clients the product with the highest rating. Also, according to the experimental results, the developed DEC with the matrix factorization approach produced better results with respect to f-measure values of 0.902, precision values of 0.896, and recall values of 0.908, respectively.

Keywords

Deep embedded clustering agglomerative matrix bilevel matching matrix factorization tversky index

Get full access to this article

View all access options for this article.

References

Zhang

Yang

and Yoshida

, DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function, Electronic Commerce Research and Applications 31 (2018), 12–23.

Chen

Y.C.

Hui

and Thaipisutikul

, A collaborative filtering recommendation system with dynamic time decay, The Journal of Supercomputing 77(1) (2021), 244–262.

Guo

Deng

Ran

Wang

and Jin

, An efficient and accurate recommendation strategy using degree classification criteria for item-based collaborative filtering, Expert Systems with Applications 164 (2021), 113756.

Jain

Nagar

Singh

P.K.

and Dhar

, EMUCF: Enhanced multistage user-based collaborative filtering through non-linear similarity for recommendation systems, Expert Systems with Applications 161 (2020), 113724.

Alhijawi

and Kilani

, A collaborative filtering recommender system using genetic algorithm, Information Processing & Management 57(6) (2020), 102310.

Panda

S.K.

Bhoi

S.K.

and Singh

, A collaborative filtering recommendation algorithm based on normalization approach, Journal of Ambient Intelligence and Humanized Computing (2020), 1–23, 2020.

Chen

Zhao

and Chen

, Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering, Complex & Intelligent Systems 6(1) (2020), 147–156.

and Han

, A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering, IEEE Systems Journal, 2020.

Netflix-Movie recommendation, https://www.kaggle.com/laowingkin/netflix-movie-recommendation, Accesed on Novemeber 2021.

10.

Bobadilla

Ortega

Hernando

and Gutiérrez

, Recommender systems survey, Knowledge-Based Systems 46 (2013), 109–132.

11.

Schafer

J.B.

Konstan

and Riedl

, Recommender systems in e-commerce, in: Proceedings of the 1st ACM conference on Electronic commerce, 1999, pp. 158–166.

12.

Burke

, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction 12(4) (2002), 331–370.

13.

Wei

Chen

Zhou

and Tang

, Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications 69 (2017), 29–39.

14.

Hwang

C.S.

and Chen

Y.P.

, Using trust in collaborative filtering recommendation, in: Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2007, pp. 1052–1060.

15.

Lee

and Lee

, Scalable deep learning-based recommendation systems, ICT Express 5(2) (2019), 84–88.

16.

Chein

Y.H.

and George

, A Bayesian model for collaborative filtering, in: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.

17.

Ungar

L.H.

and Foster

D.P.

, Clustering methods for collaborative filtering, in: Proceedings of the Workshop on Recommendation Systems, 1998.

18.

Hofmann

, Latent semantic models for collaborative filtering, ACM Transactions Information System 22(1) (2004), 89–115.

19.

Salakhutdinov

Mnih

and Hinton

, Restricted Boltzmann machines for collaborative filtering, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 791–798.

20.

Georgiev

and Nakov

, A non-IID framework for collaborative filtering with restricted Boltzmann machines, in: Proceedings of the 30th International Conference on Machine Learning, 2013, pp. 1148–1156.

21.

Oord

Dieleman

and Schrauwen

, Deep content-based music recommendation, Adv. Neural Inf. Process. Syst, 2013, 2643–2651.

22.

Yuanxin

Wenqi

Wewnge

and Zhang

, Autoencoder-based colaborative filtering, in: International Conference On Neural Information Processing, 2014, pp. 284–291.

23.

Mohammadian

Forghani

and Torshiz

M.N.

, An initialization method to improve the training time of matrix factorization algorithm for fast recommendation, Soft Computing 25(5) (2021), 3975–3987.

24.

Sanwal

and Çalişkan

, A hybrid movie recommender system and rating prediction model, International Journal of Information Technology and Applied Sciences (IJITAS) 3(3) (2021), 161–168.

25.

Xie

Girshick

and Farhadi

, Unsupervised deep embedding for clustering analysis, in: International Conference on Machine Learning, 2016, pp. 478–487.

Deep embedded clustering with matrix factorization based user rating prediction for collaborative recommendation

Abstract

Keywords

Get full access to this article

References