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Most of the existing studies focus on modeling the user\u2019s interests to estimate the click\u2010through rate (CTR). A good e\u2010commerce system not only needs precise ranking to inspire users\u2019 shopping desire but also needs feature explanation to meet the demands of shop owners. The e\u2010commerce traffic health of merchants is very important. How to effectively achieve shop owners\u2019 multiple goals still remains as an open problem. In industrial search systems, merchants\u2019 key demands mainly include two aspects. On the one hand, merchants want to know rule analysis of online traffic distribution, so as to help them understand the logistics of online traffic. On the other hand, they need relevant online traffic participation tools, which instruct them to participate. To address these issues, we propose a factor marginal effect analysis approach (FMEA) based on game theory, which can compute the contribution of one\u2010dimensional features to the enhancement of online traffic. First, we use machine learning to model the business target. Then, we improve the SHAP value algorithm, which can provide clear business insights. Finally, we calculate the marginal effect of each feature on the business outcome. In this way, we provide a traffic analysis guidance method and address merchants\u2019 participation challenges. In fact, the FMEA has been deployed in a real\u2010world Large\u2010Internet\u2010Company\u2019s App search systems and successfully serves online e\u2010commerce service to over hundreds of millions of consumers. Our approach can guide operational decisions effectively and bring +10.05% revenue for the flow index, +7.54% for the user feedback index, and +2.46% for the service index.<\/jats:p>","DOI":"10.1155\/2023\/6968854","type":"journal-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T20:09:33Z","timestamp":1697054973000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Factor Marginal Effect Analysis Approach and Its Application in E\u2010Commerce Search System"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5457-3240","authenticated-orcid":false,"given":"Yingshuai","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-5280","authenticated-orcid":false,"given":"Sachurengui","family":"Sachurengui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0647-3154","authenticated-orcid":false,"given":"Dezheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7228-7838","authenticated-orcid":false,"family":"Aziguli Wulamu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0861-9644","authenticated-orcid":false,"given":"Hashen","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1900654116"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"KohaviR. 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