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Data-Hungry Issue in Personalized Product Search
Lecture Notes in Computer Science
  • Wu Bin, Sun Yat Sen University, Guangzhou, China
  • Shangsong Liang, Sun Yat Sen University, Guangzhou, China & Mohamed bin Zayed University of Artificial Intelligence
  • Yuehong Wu, Guangdong University Technology, China
Document Type
Conference Proceeding

Product search has been receiving significant attention with the development of e-commerce. Existing works recognize the importance of personalization and focus on personalized product search. While these works have confirmed that personalization can improve the performance of product search, they all ignore the few-shot learning problems caused by personalization. Under the few-shot setting, personalized methods may suffer from the data-hungry issue. In this paper, we explore the data-hungry issue in personalized product search. We find that data-hungry issue exists under the few-shot setting caused by personalization, and degrades the performance under the few-shot setting when the input query consists of diverse intents. Furthermore, we illustrate that with such a data-hungry issue, the returned search results tend to be close to the products the user purchases most often, or the products the most users purchase in the market given the same query. The result in the further experiment confirms our conclusions.

Publication Date
  • Data-hungry issue,
  • Few-shot problem,
  • Product search

IR Conditions:

OA version (pathway a): Accepted version

12 month embargo

Must link to published article

Set statement to accompany deposit

Citation Information
W. Bin, S. Liang, and Y. Wu, "Data-Hungry Issue in Personalized Product Search", in PDCAT 2021 (Lecture Notes in Computer Science), v. 3148, p. 485-494, doi: 10.1007/978-3-030-96772-7_45