TY - GEN
T1 - On application of learning to rank for e-commerce search
AU - Santu, Shubhra Kanti Karmaker
AU - Sondhi, Parikshit
AU - Zhai, Chengxiang
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
PY - 2017/8/7
Y1 - 2017/8/7
N2 - E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of LETOR for web search has been well studied, its use for E-Com search has not yet been well explored. In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-To-cart ratios, order rates, and revenue. We study these new challenges using experiments on industry data sets and report several interesting findings that can provide guidance on how to optimally apply LETOR to E-Com search: First, popularity-based features defined solely on product items are very useful and LETOR methods were able to effectively optimize their combination with relevance-based features. Second, query a.ribute sparsity raises challenges for LETOR, and selecting features to reduce/avoid sparsity is bene.cial. .ird, while crowdsourcing is o.en useful for obtaining relevance judgments for Web search, it does not work as well for E-Com search due to difficulty in eliciting sufficiently .ne grained relevance judgments. Finally, among the multiple feedback signals, the order rate is found to be the most robust training objective, followed by click rate, while add-To-cart ratio seems least robust, suggesting that an effective practical strategy may be to initially use click rates for training and gradually shi.To using order rates as they become available.
AB - E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of LETOR for web search has been well studied, its use for E-Com search has not yet been well explored. In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-To-cart ratios, order rates, and revenue. We study these new challenges using experiments on industry data sets and report several interesting findings that can provide guidance on how to optimally apply LETOR to E-Com search: First, popularity-based features defined solely on product items are very useful and LETOR methods were able to effectively optimize their combination with relevance-based features. Second, query a.ribute sparsity raises challenges for LETOR, and selecting features to reduce/avoid sparsity is bene.cial. .ird, while crowdsourcing is o.en useful for obtaining relevance judgments for Web search, it does not work as well for E-Com search due to difficulty in eliciting sufficiently .ne grained relevance judgments. Finally, among the multiple feedback signals, the order rate is found to be the most robust training objective, followed by click rate, while add-To-cart ratio seems least robust, suggesting that an effective practical strategy may be to initially use click rates for training and gradually shi.To using order rates as they become available.
UR - http://www.scopus.com/inward/record.url?scp=85029376773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029376773&partnerID=8YFLogxK
U2 - 10.1145/3077136.3080838
DO - 10.1145/3077136.3080838
M3 - Conference contribution
AN - SCOPUS:85029376773
T3 - SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 475
EP - 484
BT - SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Y2 - 7 August 2017 through 11 August 2017
ER -