Online Review-based Product Recommendation Using Mellivora Heron Optimization-based BiLSTM Model in E-Commerce Systems

Abstract

Recently, a recommender system retrieves patterns from online reviews with the user's past interactions and provides future recommendations for users to purchase as well as consume with the upsurge development of Internet technology. By conducting sentiment analysis on a large number of user reviews on e-commerce platforms, user satisfaction can be successfully increased. However, numerous existing literature fail to extract certain implicit information and face difficulties in predicting the precise sentiment polarities of the user reviews because of the variations in sequence length, textual order, and complicated logic. To address the drawbacks of the existing techniques, the proposed research exploited the Mellivora Heron Optimization-based BiLSTM (MHO-based BiLSTM) model for product recommendation with higher quality. By incorporating the semantic ontology graph-based feature extraction involving Euclidean distance, cosine similarity, and word embeddings, along with graph embedding assists in enhancing the recommendation system. Specifically, the proposed Mellivora Heron Optimization algorithm optimally tunes the BiLSTM model to capture the context information efficiently and boosts the classifier accuracy. Moreover, a highly accurate and pertinent recommendation system for diverse scenarios is produced by combining the Spark architecture, Semantic Ontology Graph, BiLSTM classifier, and cutting-edge algorithm. With 80% of training, the MHO-based BiLSTM model for the review-based recommendation system utilizing the Amazon Product dataset obtained the accuracy, sensitivity, and specificity values of 95.29%, 95.77%, and 94.81% and outperformed other existing techniques. Similar results were found for the Amazon Video Games dataset, with values achieved during 80% of training are 96.23%, 97.20%, and 95.25%. Meanwhile, the MHO-based BiLSTM model attained the values of 96.73%, 97.71%, and 95.57%, respectively for the Amazon Video Games dataset for k-fold 10 analysis.

Keywords

recommendation system sentiment analysis mellivora heron optimization deep learning product reviews

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References

Afoudi

Lazaar

Al Achhab

(2021). Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simulation Modelling Practice and Theory, 113(4), 102375. https://doi.org/10.1016/j.simpat.2021.102375

Ahmed

S. F.

Kuldeep

S. A.

Rafa

S. J.

Fazal

Hoque

Liu

Gandomi

A. H.

(2024). Enhancement of traffic forecasting through graph neural network-based information fusion techniques. Information Fusion, 110, 1–16. https://doi.org/10.1016/j.inffus.2024.102466

Alamdari

P. M.

Navimipour

N. J.

Hosseinzadeh

Safaei

A. A.

Darwesh

(2020). A systematic study on the recommender systems in the E-commerce. IEEE Access, 8(99), 115694–115716. https://doi.org/10.1109/ACCESS.2020.3002803

Amazon Product dataset link: https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_v2/categoryFiles/Digital_Music.json.gz”)

Amazon Video Games dataset: https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_v2/categoryFiles/Video_Games.json.gz”

Darabi

Tabrizi

(2016). An ontology-based framework to model user interests. Computational Science and Computational Intelligence (CSCI), 2016 International Conference on. IEEE (pp. 398–403).

Devarajan

G. G.

Nagarajan

S. M.

Daniel

Vignesh

Kaluri

(2023). Consumer product recommendation system using adapted PSO with federated learning method. IEEE Transactions on Consumer Electronics, 70(1), 2708–2715. https://doi.org/10.1109/TCE.2023.3319374

Hashim

F. A.

Houssein

E. H.

Hussain

Mabrouk

M. S.

fAl-Atabany

(2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192(2), 84–110. https://doi.org/10.1016/j.matcom.2021.08.013

Horsburgh

Craw

Massie

(2015). Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial Intelligence, 219, 25–39. https://doi.org/10.1016/j.artint.2014.11.004

10.

Jiang

Cheng

Yang

Yan

Wang

(2019). A trust-based collaborative filtering algorithm for E-commerce recommendation system. Journal of Ambient Intelligence and Humanized Computing, 10(6), 3023–3034. https://doi.org/10.1007/s12652-018-0928-7

11.

Karthik

R. V.

Ganapathy

(2021). A fuzzy recommendation system for predicting the customer's interests using sentiment analysis and ontology in e-commerce. Applied Soft Computing, 108(3), 107396. https://doi.org/10.1016/j.asoc.2021.107396

12.

Kim

Choi

(2021). Customer satisfaction of recommender system: Examining accuracy and diversity in several types of recommendation approaches. Sustainability, 13(11), 6165. 8. https://doi.org/10.3390/su13116165

13.

Kim

H. K.

Ryu

Y. U.

Cho

Kim

J. K.

(2011). Customer-driven content recommendation over a network of customers. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 42(1), 48–56. https://doi.org/10.1109/TSMCA.2011.2147306

14.

Lai

C. H.

Tseng

K. C.

(2022). Applying deep learning models to analyze users’ aspects, sentiment, and semantic features for product recommendation. Applied Sciences, 12(4), 2118. https://doi.org/10.3390/app12042118

15.

Lee

Kim

(2021). A hybrid CNN-based review helpfulness filtering model for improving e-commerce recommendation service. Applied Sciences, 11(18), 8613. https://doi.org/10.3390/app11188613

16.

Liu

Chen

Peng

Zheng

Tang

(2020). Modelling high-order social relations for item recommendation. IEEE Transactions on Knowledge and Data Engineering, 34(9), 4385–4397. https://doi.org/10.1109/TKDE.2020.3039463

17.

Patro

S. G. K.

Mishra

B. K.

Panda

S. K.

Kumar

Long

H. V.

Taniar

Priyadarshini

(2020). A hybrid action-related K-nearest neighbour (HAR-KNN) approach for recommendation systems. IEEE Access, 8, 90978–90991. https://doi.org/10.1109/ACCESS.2020.2994056

18.

Raghuwanshi

S. K.

Kumar Pateriya

(2019). Recommendation systems: Techniques, challenges, application, and evaluation. In Soft computing for problem solving: SocProS 2017 (Vol. 2, pp. 151–164). Springer Singapore.

19.

Shambour

(2021). A deep learning based algorithm for multi-criteria recommender systems. Knowledge-based Systems, 211, 106545. https://doi.org/10.1016/j.knosys.2020.106545

20.

Shankar

Perumal

Subramanian

Ramu

Natesan

Kulkarni

V. R.

Stephan

(2024). An intelligent recommendation system in e-commerce using ensemble learning. Multimedia Tools and Applications, 83(16), 48521–48537. https://doi.org/10.1007/s11042-023-17415-1

21.

Sharma

A. K.

Bajpai

Adhvaryu

Pankajkumar

S. D.

Gordhanbhai

P. P.

Kumar

(2023). An efficient approach of product recommendation system using NLP technique. Materials Today: Proceedings, 80(7), 3730–3743. https://doi.org/10.1016/j.matpr.2021.07.371

22.

Sherstinsky

(2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404(8), 132306. https://doi.org/10.1016/j.physd.2019.132306

23.

Shoja

B. M.

Tabrizi

(2019). Customer reviews analysis with deep neural networks for e-commerce recommender systems. IEEE access, 7(99), 119121–119130. https://doi.org/10.1109/ACCESS.2019.2937518

24.

Trojovský

Dehghani

(2022). Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), 855. https://doi.org/10.3390/s22030855

25.

Turkut

Tuncer

Savran

Yılmaz

(2020). An online recommendation system using deep learning for textile products. In 2020 International congress on human-computer interaction, optimization and robotic applications (HORA) (pp. 1–4). IEEE.

26.

Vishwanathan

S. V. M.

Narasimha Murty

(2002). SSVM: A simple SVM algorithm. In Proceedings of the 2002 international joint conference on neural networks. IJCNN'02 (Cat. No. 02CH37290) (Vol. 3, pp. 2393–2398). IEEE.

27.

Wang

Zhang

Xue

S.-G.

(2020). E-commerce personalized recommendation analysis by deeply-learned clustering. Journal of Visual Communication and Image Representation, 71(6), 102735. https://doi.org/10.1016/j.jvcir.2019.102735

28.

Wei

Chen

, et al. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 29–39. https://doi.org/10.1016/j.eswa.2016.09.040

29.

Xiong

She

Xiong

(2023). A new pelican optimization algorithm for the parameter identification of memristive chaotic system. Symmetry, 15(6), 1279. Applied Energy 324 (2022): 119727. https://doi.org/10.3390/sym15061279

30.

Meng

Qiu

(2019). Sentiment analysis of comment texts based on BiLSTM. IEEE Access, 7, 51522–51532. https://doi.org/10.1109/ACCESS.2019.2909919

31.

Zhang

Z.-K.

Zhou

Zhang

Y.-C.

(2011). Tag-aware recommender systems: A state-of-the-art survey. Journal of Computer Science and Technology, 26(5), 767. https://doi.org/10.1007/s11390-011-0176-1

32.

Zhao

Liu

Yao

Yang

(2021). A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Information Processing & Management, 58(5), 102656. https://doi.org/10.1016/j.ipm.2021.102656

33.

Zhou

(2020). Product advertising recommendation in e-commerce based on deep learning and distributed expression. Electronic Commerce Research, 20(2), 321–342. https://doi.org/10.1007/s10660-020-09411-6

34.

Zhu

Yang

, et al. (2018). Product image recognition based on deep learning. JisuanjiFuzhuSheji Yu TuxingxueXuebao/Journal of Computer-Aided Design and Computer Graphics, 30(9), 1778–1784. https://doi.org/10.3724/SP.J.1089.2018.16849