Abstract
In different commercial platforms, Recommendation System (RS) is widely used for providing recommendations to users. In various areas, RS is utilized broadly and in E-Commerce sites, product recommendations are discovered during an active user interaction by RS. In recent decades, some key challenges are faced due to tremendous growth in user as well as products. Moreover, in RS, computation of right product and active user is a major task. User inclination and socio-demographic behavior are considered in existing works for recommending a product. In recommendation systems, one of the major algorithm used is Collaborative Filtering (CF) algorithms. This algorithm is simple as well as effective. However, further enhancement of recommendation result’s quality is limited by data sparsity and scalability of this technique. Previous technique’s problems are addressed effectively in proposed technique and user preference on balance feature analysis and products are evaluated. Therefore, proposed a model using the combination of deep learning technology and CF recommendation algorithm with three major stages, namely, preprocessing, representation of features and rating score prediction using DNN. At first, from log files, redundant and unnecessary data are removed in preprocessing module. There is an unwanted files like repeated tags, repeated similar products, removing invalid values, last visit and elapsed time. In feature representation stage, Quadric Polynomial Regression—QPR-based feature representation technique is used. The traditional matrix factorization algorithm is enhanced for obtaining accurate latent features. At last, DNN model is fed using these latent features as input data, which is a second stage of proposed model. Rating scores are predicted using this. From Amazon dataset, user data based on behavior is obtained and used in experimentation. There are 18,501 product reviews in Amazon product dataset. From Amazon web services, collected the dataset information that joins with administrative services. Based on metrics like F1-measure, Recall (R) and Precision (P) proposed Deep Neural Network (DNN) method is evaluated experimentally and highest value of those metrics are produced when compared with state-of-the art techniques like K-Nearest Neighbor (K-NN), Artificial Neural Networks (ANN).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davoudi A, Chatterjee M (2018) Social trust model for rating prediction in recommender systems: effects of similarity, centrality, and social ties. Online Soc Netw Media 7:1–11
Sinha BB, Dhanalakshmi R (2019) Evolution of recommender system over the time. Soft Comput 23:12169–12188
Christudas BCL, Kirubakaran E, Thangaiah PRJ (2018) An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials. Telematics Inform 35(3):520–533
Tarus JK, Niu Z, Mustafa G (2018) Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif Intell Rev 50(1):21–48
CireşAn D, Meier U, Masci J, Schmidhuber J (2012) Multi-column deep neural network for traffic sign classification. Neural Netw 32:333–338
Shekhar S, Mohan N (2021) Sentiment classification using hybrid bayes theorem support vector machine over social network. In: Smart innovations in communication and computational sciences. Advances in intelligent systems and computing, vol 1168
Shekhar S, Varshney N (2021) Hybridization of Social Spider Optimization (SSO) Algorithm with Differential Evolution (DE) using super-resolution reconstruction of video images. In: Smart innovations in communication and computational sciences. Advances in intelligent systems and computing, vol 1168
Shekhar S, Sharma DK (2020) Computational intelligence for temporal expression retrieval in code-mixed text. In: 2020 international conference on power electronics & IoT applications in renewable energy and its control (PARC). Mathura, Uttar Pradesh, India, pp 386–390
Shekhar S, Sharma DK, Sufyan Beg MM (2020) Language identification framework in code-mixed social media text based on quantum LSTM—the word belongs to which language? Modern Phys Lett B 34(6):2050086
Richardson F, Reynolds D, Dehak N (2015) Deep neural network approaches to speaker and language recognition. IEEE Sig Proc Lett 22(10):1671–1675
Liu J, Jiang Y, Li Z, Zhang X, Lu H (2016) Domain-sensitive recommendation with user-item subgroup analysis. IEEE Trans Know Data Eng 28(4):939–950
Hwangbo H, Kim YS, Cha KJ (2018) Recommendation system development for fashion retail e-commerce. Electron Commer Res Appl 28:94–101
Wei J, He J, Chen K, Zhou Y, Tang Z (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Exp Syst Appl 69:29–39
Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: The 41st international ACM SIGIR conference on research and development in information retrieval, pp 515–524
Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Exp Syst Appl 92:507–520
Tarus JK, Niu Z, Yousif A (2017) A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gen Comput Syst 72:37–48
Frémal S, Lecron F (2017) Weighting strategies for a recommender system using item clustering based on genres. Exp Syst Appl 77:105–113
Zhang L, Yang S, Zhang M (2014) E-commerce website recommender system based on dissimilarity and association rule. TELKOMNIKA Indonesian J Electric Eng 12(1):353–360
Patro SGK, Mishra BK, Panda SK, Kumar R, Long HV, Taniar D, Priyadarshini I (2020) A hybrid action-related K-nearest neighbour (HAR-KNN) approach for recommendation systems. IEEE Access 8:90978–90991
Lv J, Song B, Guo J, Du X, Guizani M (2019) Interest-related item similarity model based on multimodal data for top-N recommendation. IEEE Access
Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y (2018) DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Method 18:1–12
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 Singapore Pte Ltd.
About this paper
Cite this paper
Shekhar, S., Singh, A., Gupta, A.K. (2022). A Deep Neural Network (DNN) Approach for Recommendation Systems. In: Gao, XZ., Tiwari, S., Trivedi, M.C., Singh, P.K., Mishra, K.K. (eds) Advances in Computational Intelligence and Communication Technology. Lecture Notes in Networks and Systems, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-16-9756-2_37
Download citation
DOI: https://doi.org/10.1007/978-981-16-9756-2_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9755-5
Online ISBN: 978-981-16-9756-2
eBook Packages: EngineeringEngineering (R0)