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This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website to another without manual intervention.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Cross-domain recommender systems: A survey of the State of the Art
Ignacio Fernández-Tobías, Iván Cantador, Marius Kaminskas, and Francesco Ricci. 2012 · 2012
Earlier work this paper cites.
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013a · 2013
Earlier work this paper cites.
Exploiting Similarities among Languages for Machine Translation
Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013b · 2013
Earlier work this paper cites.
powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
Jeff Alstott, Ed Bullmore, and Dietmar Plenz. 2014 · 2014
Earlier work this paper cites.
Distributed representations of geographically situated language. In ACL . 828–834
David Bamman, Chris Dyer, and Noah A Smith. 2014 · 2014
Earlier work this paper cites.
An examination of antecedents of conversion rates of e-commerce retailers
N. Gudigantala, P. Bicen, and M. Eom. 2016 · 2014
Earlier work this paper cites.
Glove: Global Vectors for Word Representation. In EMNLP . Association for Computational Linguistics, Doha, Qatar, 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Sequence to Sequence Learning with Neural Networks. In NIPS
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
E-commerce in Your Inbox: Product Recommendations at Scale. In Proceedings of KDD ’15
Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. 2015 · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Earlier work this paper cites.
A Survey of Query Auto Completion in Information Retrieval
Fei Cai and Maarten de Rijke. 2016 · 2016
Earlier work this paper cites.
Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16) . Association for Computing Machinery, New York, NY, USA, 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1489–1501
William L Hamilton, Jure Leskovec, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
Learning bilingual word embeddings with (almost) no bilingual data. In ACL . Association for Computational Linguistics, Vancouver, Canada, 451–462
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2017 · 2017
Cited alongside, same era.
OpenNMT: Open-Source Toolkit for Neural Machine Translation. In Proc. ACL
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
Cited alongside, same era.
A neural language model for query auto-completion. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1189–1192
Dae Hoon Park and Rikio Chiba. 2017 · 2017
Cited alongside, same era.
Meta-Graph: Few shot Link Prediction via Meta Learning
Avishek Joey Bose, Ankit Jain, Piero Molino, and William L. Hamilton. 2019 · 2019
Later among the works it cites.
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. In RecSys (RecSys ’19) . ACM, New York, NY, USA, 101–109
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019 · 2019
Later among the works it cites.
Training Temporal Word Embeddings with a Compass. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 6326–6334
Valerio Di Carlo, Federico Bianchi, and Matteo Palmonari. 2019 · 2019
Later among the works it cites.
Personalized Query Auto-Completion Through a Lightweight Representation of the User Context
Manojkumar Rangasamy Kannadasan and Grigor Aslanyan. 2019 · 2019
Later among the works it cites.
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Temporal word analogies: Identifying lexical replacement with diachronic word embeddings. In Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: short papers) . 448–453
Terrence Szymanski. 2017 · 2017
Cited alongside, same era.
The EU General Data Protection Regulation (GDPR): A Practical Guide (1st ed.)
Paul Voigt and Axel von dem Bussche. 2017 · 2017
Cited alongside, same era.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. In ACL . Association for Computational Linguistics, Melbourne, Australia, 789–798
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018 · 2018
Cited alongside, same era.
Word2vec applied to Recommendation: Hyperparameters Matter. In Proceedings of RecSys ’18
Hugo Caselles-Dupré, Florian Lesaint, and Jimena Royo-Letelier. 2018 · 2018
Cited alongside, same era.
Word translation without parallel data. In International Conference on Learning Representations
Guillaume Lample, Alexis Conneau, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018 · 2018
Cited alongside, same era.
Revisiting Skip-Gram Negative Sampling Model with Regularization
Cun Mu, Guang Yang, and Zheng Yan. 2018 · 2018
Cited alongside, same era.
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation. In Proceedings of RecSys ’16
Flavian Vasile, Elena Smirnova, and Alexis Conneau. 2018 · 2018
Cited alongside, same era.
Realtime Query Completion via Deep Language Models.. In eCOM@SIGIR (CEUR Workshop Proceedings) , Jon Degenhardt, Giuseppe Di Fabbrizio, Surya Kallumadi, Mohit Kumar, Andrew Trotman, Yiu-Chang Lin, and Huasha Zhao (Eds.), Vol. 2319. CEUR-WS.org
Po-Wei Wang et al · 2018
Cited alongside, same era.
Thom Lake, Sinead A Williamson, Alexander T Hawk, Christopher C Johnson, and Benjamin P Wing. 2019 · 2019
Later among the works it cites.
Multichannel personalization: Identifying consumer preferences for product recommendations in advertisements across different media channels
Timo Schreiner, Alexandra Rese, and Daniel Baier. 2019 · 2019
Later among the works it cites.
Top sites ranking for E-commerce And Shopping in the world
SimilarWeb. 2019 · 2019
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Algolia finds $110M from Accel and Salesforce
Techcrunch. [n.d.]a · 2019
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coveo-raises-227m-at-1b-valuation
Techcrunch. [n.d.]b · 2019
Later among the works it cites.
Lucidworks raises $100M to expand in AI finds
Techcrunch. [n.d.]c · 2019
Later among the works it cites.
How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead. In Proceedings of The 3rd Workshop on e-Commerce and NLP . Association for Computational Linguistics, Seattle, WA, USA, 7–18
Jacopo Tagliabue, Bingqing Yu, and Marie Beaulieu. 2020 · 2020
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Bingqing Yu, Jacopo Tagliabue, Ciro Greco, and Federico Bianchi. 2020 · 2020
Closest in time.