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2021 | OriginalPaper | Buchkapitel

Aspect-Based Recommendation Model for Fashion Merchandising

verfasst von : Weiqing Li, Bugao Xu

Erschienen in: Advances in Digital Marketing and eCommerce

Verlag: Springer International Publishing

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Abstract

An aspect-based recommendation model (ARM) was proposed to detect local and global aspect representations in customer reviews available on ecommerce websites for fashion merchandising. This model was constructed with two independent paths to process user/item reviews simultaneously, and each path had a convolutional neural network (CNN), a long-short time memory network (LSTM) with attention mechanism to separately capture local aspect features and global aspect features. To enhance the generalization of the ARM model, the local and global aspect features from both user and item reviews were merged through mutual operations prior to the rating prediction. The Clothing, Shoes & Jewelry dataset from Amazon 5-core was used to train and test ARM. The significance of the extracted aspects regarding user preferences and item properties from the reviews were examined as opposed to several state-of-the-art fashion recommenders.

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Amazon: Amazon product data. http://jmcauley.ucsd.edu/data/amazon. Accessed on 14 Feb 2020

Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

Cheng, Z., Ding, Y., Zhu, L., Kankanhalli, M.: Aspect-aware latent factor model: rating prediction with ratings and reviews. In: Proceedings of the 2018 World Wide Web Conference, pp. 639–648 (2018)

Chin, J.Y., Joty, S., Zhao, K., Cong, G.: ANR: aspect-based neural recommender. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 147–156 (2018)

Google: word2vec. https://code.google.com/archive/p/word2vec/. Accessed on 29 July 2013

Guan, X., et al.: Attentive aspect modeling for review-aware recommendation. ACM Trans. Inf. Syst. 37(3), 1–27 (2019)CrossRef

Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model: mutual learning between ratings and reviews. In: Proceedings of the 2018 World Wide Web Conference, pp. 773–782 (2018)

Liu, D., Li, J., Du, B., Chang, J., Gao, R.: DAML: dual attention mutual learning between ratings and reviews for item recommendation. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 344–352 (2019)

Ma, Q., Yu, L., Tian, S., Chen, E., Ng, W.W.Y.: Global-local mutual attention model for text classification. IEEE/ACM Trans. Audio Speech Lang. Process. 27(12), 2127–2139 (2019)CrossRef

Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)CrossRef

Seo, S., Huang, J., Yang, H., Liu, Y.: Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the 11th ACM Conference on Recommender Systems, pp. 297–305 (2017)

Song, K., Gao, W., Feng, S., Wang, D., Wong, K.F., Zhang, C.: Recommendation vs sentiment analysis: a text-driven latent factor model for rating prediction with cold-start awareness. In: Proceedings of the Twenty-Sixth International Joint Conferences on Artificial Intelligence, pp. 2744–2750 (2017)

Wang, N., Wang, H., Jia, Y., Yin, Y.: Explainable recommendation via multi-task learning in opinionated text data. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2018)

Wu, Y., Ester, M.: FLAME: A Probabilistic model combining aspect based opinion mining and collaborative filtering. In: WSDM 2015 – Proceedings of the 8th International Conference on Web Search and Data Mining, pp. 199–208 (2015)

Zeng, J., Ma, X., Zhou, K.: Enhancing attention-based LSTM with position context for aspect-level sentiment classification. IEEE Access 7, 20462–20471 (2019)CrossRef

Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. Int. Conf. Mach. Learn. 3, 2048–2057 (2015)

Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 83–92 (2014)

Titel: Aspect-Based Recommendation Model for Fashion Merchandising
verfasst von: Weiqing Li
Bugao Xu
Verlag: Springer International Publishing
Buch: Advances in Digital Marketing and eCommerce
Print ISBN: 978-3-030-76519-4

Electronic ISBN: 978-3-030-76520-0

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-76520-0_25

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