Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

- Hanafi, Nanna Suryana, Abd Samad Bin Hasan Basari


E-commerce is one of the most popular service applications in the world in the last decade. It has become a revolutionary model from traditional shopping transaction to entire internet commerce. E-commerce needs essential artificial intelligence (AI) to provide the customer with information about a product, called a recommendation machine. Collaborative filtering is a model of a recommendation algorithm that relies on rating as the fundamental calculation to make a recommendation. It has been successfully implemented in e-commerce. Even so, this model has a weakness in sparse product data in which the rating number is very low or sparse. Mostly, only less than 3% of the total user population rate a product, leading to the rise of sparse data. A text sentence document is a part of customers’ feedback that can be converted into a product rating.  According to a traditional approach, bag of word and lexicon model are ignored in a contextual approach. This experiment, it developed a new model to increase the contextuality of text sentences, leading to a more effective rating prediction. We employed a kind of convolutional neural network to generate item latent factor vectors that could be incorporated with probabilistic matrix factorization to make rating prediction. Our method outperformed several previous works based on a metric evaluation using the Root Mean Squared Error (RMSE). In this experiment, we analyzed MovieLens and IMDB datasets, which contained a movie product review.


E-commerce; recommender system; convolutional; text sentence; sparse data; product recommendation.

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C. G. Uribe, N. Hunt, and N. Inc, “The Netflix Recommender System: Algorithms, Business Value, and Innovation,†ACM Trans. Inf. Syst., vol. 6, no. 4, p. 19, 2015.

J. Davidson, B. Liebald, J. Liu, P. Nandy, and T. Van Vleet, “The YouTube Video Recommendation System,†in ACM Recsys 2010, 2010, no. August, pp. 293–296.

J. Ben Schafer, J. Konstan, and J. Riedl, “E-commerce recommendation applications,†Appl. Data Min. to Electron. …, pp. 115–153, 2001.

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.

E. Çano and M. Morisio, “Hybrid recommender systems: A systematic literature review,†Intell. Data Anal., vol. 21, no. 6, pp. 1487–1524, 2017.

M. Elahi, F. Ricci, and N. Rubens, “A survey of active learning in collaborative filtering recommender systems,†Comput. Sci. Rev., vol. 20, pp. 29–50, 2016.

F. Belletti, K. Lakshmanan, W. Krichene, Y.-F. Chen, and J. Anderson, “Scalable Realistic Recommendation Datasets Through Fractal Expansions,†arXiv Prepr. arXiv1901.08910, 2019.

C. C. Aggarwal, Recommender systems: The Textbook. London: Springer International Publishin, Switzerland, 2016.

D. Kotkov, S. Wang, and J. Veijalainen, “A survey of serendipity in recommender systems,†Knowledge-Based Syst., vol. 111, no. August, pp. 180–192, 2016.

J. Wei, J. He, K. Chen, Y. Zhou and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,†Expert Syst. Appl., vol. 69, pp. 1339–1351, 2017.

A. van den Oord, S. Dieleman, and B. Schrauwen, “Deep Content-Based Music Recommendation,†NIPS, pp. 2643–2651, 2013.

X. Wang and Y. Wang, “Improving Content-based and Hybrid Music Recommendation Using Deep Learning,†Proc. 22Nd ACM Int. Conf. Multimedia., pp. 627–636, 2014.

S. Jaradat, “Deep Cross-Domain Fashion Recommendation,†Proc. Elev. ACM Conf. Recomm. Syst. - RecSys ’17, pp. 407–410, 2017.

K. Park, J. Lee, and J. Choi, “Deep Neural Networks for News Recommendations,†CIKM’17, November. 6-10, 2017, Singapore, 2017.

X. Wang et al., “Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration,†Proc. 23rd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. - KDD ’17, pp. 2051–2059, 2017.

F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook, 2nd ed. London: Springer New York Heidelberg Dordrecht London, 2015.

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

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems,†Am. Lab., vol. 37, no. 10, p. 4, 2005.

S. Zhang, W. Wang, J. Ford, and F. Makedon, “Learning from Incomplete Ratings Using Non-negative Matrix Factorization,†Proc. 2006 SIAM Int. Conf. Data Min., pp. 549–553, 2006.

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

L. Zheng, A Survey and Critique of Deep Learning on Recommender Systems, 1st ed., no. September. Chicago: University Of Illinois At Chicago, 2016.

R. J. R. Filho, J. Wehrmann, and R. C. Barros, “Leveraging Deep Visual Features for Content-based Movie Recommender Systems,†Proc. Int. Jt. Conf. Neural Networks, vol. 2017-May, pp. 604–611, 2017.

X. Dong, L. Yu, Z. Wu, Y. Sun, L. Yuan, and F. Zhang, “A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems,†Aaai, pp. 1309–1315, 2017.

P. Covington, J. Adams, and E. Sargin, “Deep Neural Networks for YouTube Recommendations,†Proc. 10th ACM Conf. Recomm. Syst. - RecSys ’16, pp. 191–198, 2016.

H. Wang and D. Yeung, “Collaborative Deep Learning for Recommender Systems arXiv : 1409. 2944v1 [ cs . LG ] 10 Sep 2014,†no. July 2015, 2014.

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.

L. Zheng, V. Noroozi, and P. S. Yu, “Joint Deep Modeling of Users and Items Using Reviews for Recommendation,†in WSDM 2017, 2017, no. February, pp. 425–434.

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.

D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional Matrix Factorization for Document Context-Aware Recommendation,†Proc. 10th ACM Conf. Recomm. Syst. - RecSys ’16, pp. 233–240, 2016.

J. Pennington, R. Socher, and C. Manning, “Glove: Global Vectors for Word Representation,†Proc. 2014 Conf. Empir. Methods Nat. Lang. Process., pp. 1532–1543, 2014.

E. Çano and M. Morisio, “A deep learning architecture for sentiment analysis,†Proc. Int. Conf. Geoinformatics Data Anal. - ICGDA ’18, no. April, pp. 122–126, 2018.

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

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.

J. McAuley and J. Leskovec, “Hidden factors and hidden topics,†Proc. 7th ACM Conf. Recomm. Syst. - RecSys ’13, pp. 165–172, 2013.



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