Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System

- Hanafi - University of Amikom Yogyakarta, Condongcatur Depok, Sleman, Indonesia
Anik Sri Widowati - University of Amikom Yogyakarta, Condongcatur Depok, Sleman, Indonesia
- Jaeni - University of Amikom Yogyakarta, Condongcatur Depok, Sleman, Indonesia
Jack Febrian Rusdi - Sekolah Tinggi Teknologi Bandung, Bandung, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.3.1233

Abstract


E-commerce has been the most important service in the last two decades. E-commerce services influence the growth of the economic impact worldwide. A recommender system is an essential mechanism for calculating product information for e-commerce users. The successfulness of recommender system adoption influences the target revenue of an e-commerce company. Collaborative filtering (CF) is the most popular algorithm for creating a recommender system. CF applied a matrix factorization mechanism to calculate the relationship between user and product using rating variable as intersection value between user and product. However, the number of ratings is very sparse, where the number of ratings is less than 4%. Product Document is the product side information representation. The document aims to advance the effectiveness of matrix factorization performance. This research considers to the enhancement of document context using LSTM with an attention mechanism to capture a contextual understanding of product review and incorporate matrix factorization based on probabilistic matrix factorization (PMF) to produce rating prediction. This study employs a real dataset using MovieLens dataset ML.1M and Amazon information video (AIV) to observe our ATT-PMF model. Movielens dataset represents of number sparse rating that only contains below 4% (ML.1M). Our experiment report shows that ATT-PMF outperforms more than 2% on average than previous work. Moreover, our model is also suitable to implement on huge datasets. For further research, enhancement of product document context will be a good factor in eliminating sparse data problems in big data problems.


Keywords


Sparsity data; recommender system; matrix factorization; e-commerce; attention mechanism; PMF.

Full Text:

PDF

References


Hanafi, R. Widyawati, and A. S. Widowati, “Effect of service quality and online servicescape toward customer satisfaction and loyalty mediated by perceived value,†IOP Conf. Ser. Earth Environ. Sci., vol. 704, no. 1, 2021, doi: 10.1088/1755-1315/704/1/012011.

Hanafi, N. Suryana, and A. Sammad, “An Understanding and Approach Solution for Cold Start Problem Associated with Recommender System : A Literature Review,†J. Theor. Appl. Inf. Technol., vol. 96, no. 09, pp. 2677–2695, 2018.

Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,†IEEE, vol. 40, no. 8, pp. 42–49, 2009.

Hanafi, N. Suryana, and A. Samad, “Deep Learning for Recommender System Based on Application Domain Classification Perspective : a Review,†J. Theor. Appl. Inf. Technol., vol. 96, no. 14, pp. 4513–4529, 2018.

Hanafi, N. Suryana, and A. S. H. Basari, “Exploit Multi Layer Deep Learning and Latent Factor to Handle Sparse Data for E-commerce Recommender System,†SCITEPRESS, no. Conrist 2019, pp. 343–351, 2020, doi: 10.5220/0009910603430351.

B. M. Sarwar, G. Karypis, J. a Konstan, and J. T. Riedl, “Application of Dimensionality Reduction in Recommender System - A Case Study,†Architecture, vol. 1625, pp. 264–8, 2000, doi: 10.1.1.38.744.

Hanafi, N. Suryana, and A. S. H. Basari, “Generate Contextual Insight of Product Review Using Deep LSTM and Word Embedding,†J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012006.

Hanafi, N. Suryana, and A. S. B. H. Basari, “Convolutional-NN and word embedding for making an effective product recommendation based on enhanced contextual understanding of a product review,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 3, 2019, doi: 10.18517/ijaseit.9.3.8843.

Hanafi, N. Suryana, and A. S. H. Basari, “Deep Contextual of Document Using Deep LSTM Meet Matrix Factorization to Handle Sparse Data: Proposed Model,†J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012002.

Hanafi, N. Suryana, and A. S. B. H. Basari, “Convolutional-NN and word embedding for making an effective product recommendation based on enhanced contextual understanding of a product review,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 3, pp. 1063–1070, 2019, doi: 10.18517/ijaseit.9.3.8843.

G. Ling, M. R. Lyu, and I. King, “Ratings meet reviews, a combined approach to recommend,†Proc. 8th ACM Conf. Recomm. Syst. - RecSys ’14, pp. 105–112, 2014, doi: 10.1145/2645710.2645728.

R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization.,†Proc. Adv. Neural Inf. Process. Syst. 20 (NIPS 07), pp. 1257–1264, 2007, doi: 10.1145/1390156.1390267.

C. C. Aggarwal, Machine Learning for Text. NEWYORK: Springer International Publishing AG, 2018.

J. Liu and D. Wang, “PHD : A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems,†in ACML, 2017, pp. 224–239.

C. Wang and D. M. Blei, “Collaborative Topic Modeling for Recommending Scientific Articles,†Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. - KDD ’11, p. 448, 2011, doi: 10.1145/2020408.2020480.

Hanafi, E. Pujastuti, A. Laksito, A. Arfriandi, R. Hardi, and R. Perwira, “Handling Sparse Rating Matrix for E-commerce Recommender System Using Hybrid Deep Learning Based on LSTM , SDAE and Latent Factor,†vol. 15, no. 2, pp. 379–393, 2022, doi: 10.22266/ijies2022.0430.35.

Hanafi, N. Suryana, and A. S. B. H. BASARI, “Recommender System Based Tensor Candecomp Parafact Algorithm-ALS to Handle Sparse Data In Food Commerce Information Services,†IJSSST, pp. 1–9, 2019, doi: 10.5013/IJSSST.a.19.06.60.

H. Wang, N. Wang, and D.-Y. Yeung, “Collaborative Deep Learning for Recommender Systems,†in KDD conference, 2015, pp. 1235–1244, doi: 10.1145/2783258.2783273.

J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem,†2016 IEEE 14th Intl Conf Dependable, Auton. Secur. Comput. 14th Intl Conf Pervasive Intell. Comput. 2nd Intl Conf Big Data Intell. Comput. Cyber Sci. Technol. Congr., pp. 874–877, 2016, doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.149.

D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional Matrix Factorization for Document Context-Aware Recommendation,†in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 233–240, doi: 10.1145/2959100.2959165.

Hanafi and B. M. Aboobaider, “Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System,†Comput. Intell. Neurosci., vol. 2021, no. 1, 2021, doi: https://doi.org/10.1155/2021/8751173 Research.

X. Wang, X. Yang, L. Guo, Y. Han, F. Liu, and B. Gao, “Exploiting Social Review-Enhanced Convolutional Matrix Factorization for Social Recommendation,†IEEE Access, vol. 7, pp. 82826–82837, 2019, doi: 10.1109/ACCESS.2019.2924443.

B. Zhang, H. Zhang, X. Sun, G. Feng, and C. He, “Integrating an attention mechanism and convolution collaborative filtering for document context-aware rating prediction,†IEEE Access, vol. 7, pp. 3826–3835, 2019, doi: 10.1109/ACCESS.2018.2887100.

S. Hochreiter and J. Urgen Schmidhuber, “Lstm,†Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.

Y. Kim, “Convolutional Neural Networks for Sentence Classification,†in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1746–1751, doi: 10.3115/v1/D14-1181.

Hanafi, A. Pranolo, and Y. Mao, “Cae-covidx: Automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder,†Int. J. Adv. Intell. Informatics, vol. 7, no. 1, pp. 49–62, 2021, doi: 10.26555/ijain.v7i1.577.

J. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,†Adv. Neural Inf. Process. Syst., vol. 2015-Janua, pp. 577–585, 2015.

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872.

F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,†ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, pp. 19:1--19:19, 2015, doi: 10.1145/2827872.

J. McAuley, “Amazon Product Data,†2021. .

A. Gunawardana, “A Survey of Accuracy Evaluation Metrics of Recommendation Tasks,†vol. 10, pp. 2935–2962, 2009.

Hanafi, N. Suryana, and A. S. B. H. Bashari, “Paper survey and example of collaborative filtering implementation in recommender system,†J. Theor. Appl. Inf. Technol., vol. 95, no. 16, 2017.