@inproceedings{guo-etal-2020-deep,
title = "Deep Learning-based Online Alternative Product Recommendations at Scale",
author = "Guo, Mingming and
Yan, Nian and
Cui, Xiquan and
Wu, San He and
Ahsan, Unaiza and
West, Rebecca and
Al Jadda, Khalifeh",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 3rd Workshop on e-Commerce and NLP",
month = jul,
year = "2020",
address = "Seattle, WA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ecnlp-1.3",
doi = "10.18653/v1/2020.ecnlp-1.3",
pages = "19--23",
abstract = "Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers{'} needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12{\%} in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.",
}
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<abstract>Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers’ needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12% in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.</abstract>
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%0 Conference Proceedings
%T Deep Learning-based Online Alternative Product Recommendations at Scale
%A Guo, Mingming
%A Yan, Nian
%A Cui, Xiquan
%A Wu, San He
%A Ahsan, Unaiza
%A West, Rebecca
%A Al Jadda, Khalifeh
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 3rd Workshop on e-Commerce and NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA
%F guo-etal-2020-deep
%X Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers’ needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12% in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.
%R 10.18653/v1/2020.ecnlp-1.3
%U https://aclanthology.org/2020.ecnlp-1.3
%U https://doi.org/10.18653/v1/2020.ecnlp-1.3
%P 19-23
Markdown (Informal)
[Deep Learning-based Online Alternative Product Recommendations at Scale](https://aclanthology.org/2020.ecnlp-1.3) (Guo et al., ECNLP 2020)
ACL
- Mingming Guo, Nian Yan, Xiquan Cui, San He Wu, Unaiza Ahsan, Rebecca West, and Khalifeh Al Jadda. 2020. Deep Learning-based Online Alternative Product Recommendations at Scale. In Proceedings of the 3rd Workshop on e-Commerce and NLP, pages 19–23, Seattle, WA, USA. Association for Computational Linguistics.