Abstract
In fashion electronic commerce services, two item-based recommendation approaches, image similarity-based and click likelihood-based, are used to improve the revenue of a website. To improve accuracy, in this paper, we propose a hybrid model, a deep neural network (DNN) that predicts click probability of a target fashion item by incorporating both image similarity and click likelihood. To create an image similarity feature, we acquire a latent image feature through a CNN-based classification of fashion color, type and pattern. To create a click likelihood feature, we calculate matrix factorization (MF) and use decomposed item features as latent click log feature. To solve a cold-start problem (recommendation of new items), we complement the latent log features of new items with those of existing ones. An offline evaluation shows that the accuracy of proposed model (both log and image) improved by 14% compared with matrix factorization (log only) and 56% the image-only model. Moreover, the complement of latent log features changes the new item ratio to six times.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarwal, P., Vempati, S., Borar, S.: Personalizing similar product recommendations in fashion e-commerce. ArXiv abs/1806.11371 (2018)
Bell, S., Bala, K.: Learning visual similarity for product design with convolutional neural networks. ACM Trans. Graph. 34, 98:1–98:10 (2015)
Boureau, Y.L., Bach, F.R., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2559–2566 (2010)
Fard, K., Nilashi, M., Salim, N.: Recommender system based on semantic similarity. Int. J. Electr. Comput. Eng. (IJECE), 3 (2013)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26thInternational Joint Conference on Artificial Intelligence, pp. 1725–1731. IJCAIf17, AAAI Press (2017)
He, R., McAuley, J.: VBPR: Visual Bayesian personalized ranking from implicit feedback. In: AAAI, pp. 144–150 (2016)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272 (2008)
Huang, J., Feris, R., Chen, Q., Yan, S.: Cross-domain image retrieval with a dual attribute-aware ranking network. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1062–1070 (2015)
Iwata, T., Watanabe, S., Sawada, H.: Fashion coordinates recommender system using photographs from fashion magazines. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 2262–2267 (2011)
Jing, Y., et al.: Visual search at pinterest, pp. 1889–1898 (2015)
Kavukcuoglu, K., Sermanet, P., lan Boureau, Y., Gregor, K., Mathieu, M., Cun, Y.L.: Learning convolutional feature hierarchies for visual recognition. Adv. Neural Inf. Process. Syst. 23, 1090–1098 (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR (2015)
Kiros, R., Salakhutdinov, R., Zemel, R.: Multimodal neural language models. In: Proceedings of the 31st International Conference on International Conference on Machine Learning, ICMLf14 (2014)
Kula, M.: Metadata embeddings for user and item cold-start recommendations. In: CBRecSys@RecSys 2015 (2015)
Manfredi, M., Grana, C., Calderara, S., Cucchiara, R.: A complete system for garment segmentation and color classification. J. Mach. Vis. Appl. 25(4), 955–969 (2014)
Packer, C., McAuley, J.J., Ramisa, A.: Visually-aware personalized recommendation using interpretable image representations. ArXiv abs/1806.09820 (2018)
Sembium, V., Rastogi, R., Saroop, A., Merugu, S.: Recommending product sizes to customers. In: Proceedings of the Eleventh ACM Conference on Recommender Systems (2017)
Wang, X., Zhang, T.: Clothes search in consumer photos via color matching and attribute learning. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1353–1356 (2011)
Ying, Y.: The personalized recommendation algorithm based on item semantic similarity. In: Communication Systems and Information Technology, pp. 999–1004 (2011)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 27, 3320–3328 (2014)
Yu, W., Zhang, H., He, X., Chen, X., Xiong, L., Qin, Z.: Aesthetic-based clothing recommendation. In: WWW (2018)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ito, T. et al. (2021). Deep Neural Network Incorporating CNN and MF for Item-Based Fashion Recommendation. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-69886-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69885-0
Online ISBN: 978-3-030-69886-7
eBook Packages: Computer ScienceComputer Science (R0)