Author:
Wang Jingang,Tian Junfeng,Qiu Long,Li Sheng,Lang Jun,Si Luo,Lan Man
Abstract
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
8 articles.
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2. An Effective Model based on CNN - LSTM for E-Commerce Product Title Classification;2024 International Conference on Expert Clouds and Applications (ICOECA);2024-04-18
3. Unsupervised Product Title Optimization Based on Search Behavior Knowledge in E-commerce;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18
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5. Unsupervised Title Generation from Unpaired Data with N-Gram Discriminators;2022 7th International Conference on Signal and Image Processing (ICSIP);2022-07-20