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    <abstract>Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. As manual construction of channels is time-consuming due to billions of products as well as dynamic and diverse customers’ interests, it is necessary to leverage AI techniques to automatically construct channels for certain usage scenarios and even discover novel topics. To be specific, we first frame the channel construction task as a two-step problem, i.e., scene-based topic generation and product clustering, and propose an E-commerce Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated production, consisting of scene-based topic generation model for the e-commerce domain, product clustering on the basis of topic similarity, as well as quality control based on automatic model filtering and human screening. Extensive offline experiments and online A/B test validates the effectiveness of such a novel product form as well as the proposed system. In addition, we also introduce the experience of deploying the proposed system on a real-world e-commerce recommendation platform.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-industry.28</identifier>
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%0 Conference Proceedings
%T Automatic Scene-based Topic Channel Construction System for E-Commerce
%A Lin, Peng
%A Zou, Yanyan
%A Wu, Lingfei
%A Ma, Mian
%A Ding, Zhuoye
%A Long, Bo
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F lin-etal-2022-automatic-scene
%X Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. As manual construction of channels is time-consuming due to billions of products as well as dynamic and diverse customers’ interests, it is necessary to leverage AI techniques to automatically construct channels for certain usage scenarios and even discover novel topics. To be specific, we first frame the channel construction task as a two-step problem, i.e., scene-based topic generation and product clustering, and propose an E-commerce Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated production, consisting of scene-based topic generation model for the e-commerce domain, product clustering on the basis of topic similarity, as well as quality control based on automatic model filtering and human screening. Extensive offline experiments and online A/B test validates the effectiveness of such a novel product form as well as the proposed system. In addition, we also introduce the experience of deploying the proposed system on a real-world e-commerce recommendation platform.
%R 10.18653/v1/2022.emnlp-industry.28
%U https://aclanthology.org/2022.emnlp-industry.28
%U https://doi.org/10.18653/v1/2022.emnlp-industry.28
%P 272-284

Markdown (Informal)

[Automatic Scene-based Topic Channel Construction System for E-Commerce](https://aclanthology.org/2022.emnlp-industry.28) (Lin et al., EMNLP 2022)

• Automatic Scene-based Topic Channel Construction System for E-Commerce (Lin et al., EMNLP 2022)

ACL

• Peng Lin, Yanyan Zou, Lingfei Wu, Mian Ma, Zhuoye Ding, and Bo Long. 2022. Automatic Scene-based Topic Channel Construction System for E-Commerce. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 272–284, Abu Dhabi, UAE. Association for Computational Linguistics.