Sapna
ABSTRACT
The use of Artificial Intelligence (AI) techniques has become prevalent with almost all e-commerce sites. Following the trend, today's Fashion recommender systems are no exception, which widely use AI for catering to multiple use-cases. [3][4]. However this is still quite far from replicating the personalized shopping experience in a fashion store. Through this paper and demonstration we try and replicate the offline experience in an online world by using a Natural Language chatbot powered by multiple deep learning models. Athena, the personal shopping adviser in the presented system enables user interaction in natural language via a web-browser based application (extensible to a mobile application). The application is powered by two underlying machine learning components - Recommendence and Fashionsence. The recommendation system Recommendence is an ensemble of deep learning based and graph based collaborative filtering recommenders and is able to provide personalized fashion recommendations to the users. The Fashionsence component is a deep learning based model which can learn a fashion sense from online, trend and stylist data to recommend a trending fashionable set of items from the product inventory. This entire system is integrated via an orchestrating layer.
- {n. d.}. tf.losses.mean_pairwise_squared_error | TensorFlow. https://www.tensorflow.org/api_docs/python/tf/losses/mean_pairwise_squared_error. (Accessed on 09/29/2018).Google Scholar
- Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. 2011. Context-Aware Recommender Systems. AI Magazine 32, 3 (oct 2011), 67.Google ScholarCross Ref
- Chester Gray, Meghan Beattie, Helena Belay, Sarah Hill, and Nicolette Lerch. 2015. Personalized online search for fashion products. In 2015 Systems and Information Engineering Design Symposium. IEEE.Google ScholarCross Ref
- Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S Davis. 2017. Learning fashion compatibility with bidirectional lstms. In Proceedings of the 2017 ACM on Multimedia Conference. ACM, 1078--1086. Google ScholarDigital Library
- Malcolm Slaney and Michael Casey. 2008. Locality-sensitive hashing for finding nearest neighbors {lecture notes}. IEEE Signal processing magazine 25, 2 (2008), 128--131.Google ScholarCross Ref
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1--9.Google ScholarCross Ref
Index Terms
- Recommendence and Fashionsence: Online Fashion Advisor for Offline Experience
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