Deep convolutional neural networks for product recommendation

Deep convolutional neural networks for product recommendation

N. Lakshmipathi Anantha Bhanu Prakash Battula 

Acharya Nagarjuna University, Department of IT, VFSTR Deemed to be University, Vadlamudi, India

Department of CSE, Thirumala Engineering College, Jonnalagadda (V), India

Corresponding Author Email:
| |
| | Citation



Recommender Systems are big breakthrough for the web enabled systems as Recommender Systems have the capability to analyze the behavior patterns of the user. And these systems are accomplishing the task of recommending the products the users are interested in. Existed models grabbing the insights of the users and items patterns will give satisfactory results to the users. This paper uses pretrained models to extract the knowledge from the data using the concept of transfer learning. Our models use the knowledge of pre-trained models to extract patterns between users and items. To facilitate this objective, in this paper we presented our approach to generate recommendations in two phases. In the Classification phase, classification of product images and its experimental analysis following, the Ranking phase to rank the product images to the user and its experimental analysis are discussed. The result analysis discussed in this paper achieved promising results.


recommender system, convolutional neural network, content-based filtering, ranking

1. Introduction
2. Related work
3. Classification phase
4. Ranking phase
5. Conclusion

Baldi P. (2012). Autoencoders, unsupervised learning, and deep architectures. Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 37-49.

Breese J. S., Heckerman D., Kadie C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proc. of the 14th Annual Conf. on Uncertainty in Artificial Intelligence, pp. 43-52.

Cheng Z., Hurley N. (2009). Effective diverse and obfuscated attacks on model-based recommender systems. RecSys ’09: Proceedings of the Third ACM Conference on Recommender Systems, pp. 141-148.

Georgiev K., Nakov P. (2013). A non-IID framework for collaborative filtering with restricted Boltzmann machines. ICML, Vol. 28, pp. 1148-1156.

He K. M., Zhang X. Y., Ren S. Q., Sun J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778.

He X. N., Liao L. Z., Zhang H. W., Nie L. Q., Hu X., Chua T. S. (2017). Neural collaborative filtering. WWW Companion, pp. 173-182.

Hummel H. G. K., Van den Berg B., Berlanga A. J., Drachsler H., Janssen J., Nadolski R. J., Koper E. J. R. (2007). Combining social- and information-based approaches for personalised recommendation on sequencing learning activities. International Journal of Learning Technology, Vol. 3, No. 2, pp. 152-168.

Krizhevsky A., Sutskever I., Hinton G. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, Vol. 25, No. 2, pp. 1097-1105.

Lecun Y., Bottou L., Bengio Y., Haffner P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324.

Li H. (2014). Learning to rank for information retrieval and natural language processing. Synthesis Lectures on Human Language Technologies, Vol. 7, No. 3, pp. 1-121.

Meteren R. V., Someren M. V. (2000). Using content-based filtering for recommendation. Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pp. 47-56.

Montaleão Brum Alves R. (2017). Information retrieval dataset - internet movie database (IMDB). via Mendeley Data.

Schafer J. B., Frankowski D., Herlocker J., Sen S. (2007). Collaborative filtering recommender systems. The Adaptive Web, pp. 291-324.

Sedhain S., Menon A. K., Sanner S., Xie L. X. (2015). Autorec: Autoencoders meet collaborative filtering. WWW Companion, Vol. 3, pp. 111–112.

Simonyan K., Zisserman A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, pp. 1409.1556.

Szegedy C., Liu W., Jia Y. Q., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9.

Zeiler M. D., Fergus R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, pp. 818-833.