Recommendence and Fashionsence-Reference-Cited by-同舟云学术

Recommendence and Fashionsence

Published:2019-01-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM India Joint International Conference on Data Science and Management of Data
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Author:

Sapna ¹,Chakraborty Ria¹,M. Anagha¹,Vats Kartikeya¹,Baradia Khyati¹,Khan Tanveer¹,Sarkar Sandipan¹,Roychowdhury Sujoy¹

Affiliation:

1. IBM India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3297001.3297035

Reference6 articles.

1. {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). {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).

2. Context-Aware Recommender Systems

3. Personalized online search for fashion products

4. Learning Fashion Compatibility with Bidirectional LSTMs

5. Malcolm Slaney and Michael Casey. 2008. Locality-sensitive hashing for finding nearest neighbors {lecture notes}. IEEE Signal processing magazine 25 2 (2008) 128--131. Malcolm Slaney and Michael Casey. 2008. Locality-sensitive hashing for finding nearest neighbors {lecture notes}. IEEE Signal processing magazine 25 2 (2008) 128--131.

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