User-Inspired Posterior Network for Recommendation Reason Generation

Zhan, Haolan; Zhang, Hainan; Chen, Hongshen; Shen, Lei; Lan, Yanyan; Ding, Zhuoye; Yin, Dawei

doi:10.1145/3397271.3401324

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Generating product selling points in e‐commerce can be regarded as a task to introduce the uniqueness of products to customers to help them make informed purchasing decisions Zhan et al. (2020). It is normally implemented as a critical component for the recommendation system by providing valuable recommendation reasons.…”

Section: Application Descriptionmentioning

confidence: 99%

Intelligent online selling point extraction and generation for e‐commerce recommendation

et al. 2023

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During the past decade, great significant advancements have been witnessed in the domain of automatic product description generation. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the intelligent online selling point extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. In addition, an interactive tool is released for merchants in JD.COM, which additionally allow them to select and customize the selling points with flexibility. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than four million products). So far, it has generated more than 0.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labor. These IOSPE-generated selling points have increased the click-through rate (CTR) by 1.89% and the average duration the customers spent on the products by more than 2.03% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.

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Section: Application Descriptionmentioning

confidence: 99%

Intelligent online selling point extraction and generation for e‐commerce recommendation

et al. 2023

Self Cite

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show abstract

“…Generating product selling points in e-commerce can be regarded as a task to introduce the uniqueness of products to customers to help them make informed purchasing decisions (Zhan et al 2020). It is normally implemented as a critical component for the recommendation system by providing valuable recommendation reasons.…”

Section: Application Descriptionmentioning

confidence: 99%

Intelligent Online Selling Point Extraction for E-commerce Recommendation

Guo¹,

Wang

Zhao

et al. 2022

AAAI

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In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the Intelligent Online Selling Point Extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 million products). So far, it has generated more than 1.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labour. These IOSPE generated selling points have increased the click-through rate (CTR) by 1.89% and the average duration the customers spent on the products by more than 2.03% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.

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“…Interpretability: The most common view of interpretability in RS is to increase the transparency of algorithms [14], [15], [40], [43], [164], which is especially important in health RS. Reliable explanations can greatly improve end-users' confidence in the recommendation results [126].…”

Section: Challengesmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

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User-Inspired Posterior Network for Recommendation Reason Generation

Cited by 8 publications

References 7 publications

Intelligent online selling point extraction and generation for e‐commerce recommendation

Intelligent online selling point extraction and generation for e‐commerce recommendation

Intelligent Online Selling Point Extraction for E-commerce Recommendation

An Overview of Recommendation Techniques and Their Applications in Healthcare

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