[2409.17456] Long or Short or Both? An Exploration on Lookback Time Windows of Behavioral Features in Product Search Ranking
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Computer Science > Information Retrieval

arXiv:2409.17456 (cs)
[Submitted on 26 Sep 2024]

Title:Long or Short or Both? An Exploration on Lookback Time Windows of Behavioral Features in Product Search Ranking

Authors:Qi Liu, Atul Singh, Jingbo Liu, Cun Mu, Zheng Yan, Jan Pedersen
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Abstract:Customer shopping behavioral features are core to product search ranking models in eCommerce. In this paper, we investigate the effect of lookback time windows when aggregating these features at the (query, product) level over history. By studying the pros and cons of using long and short time windows, we propose a novel approach to integrating these historical behavioral features of different time windows. In particular, we address the criticality of using query-level vertical signals in ranking models to effectively aggregate all information from different behavioral features. Anecdotal evidence for the proposed approach is also provided using live product search traffic on this http URL.
Comments: Published in ACM SIGIR Workshop on eCommerce 2024
Subjects: Information Retrieval (cs.IR)
Cite as: arXiv:2409.17456 [cs.IR]
  (or arXiv:2409.17456v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2409.17456

Submission history

From: Qi Liu [view email]
[v1] Thu, 26 Sep 2024 01:18:29 UTC (4,454 KB)
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