Aggregated Customer Engagement Model
- URL: http://arxiv.org/abs/2108.07872v1
- Date: Tue, 17 Aug 2021 20:58:10 GMT
- Title: Aggregated Customer Engagement Model
- Authors: Priya Gupta and Cuize Han
- Abstract summary: E-commerce websites use machine learned ranking models to serve shopping results to customers.
New or under-impressed products do not have enough customer engagement signals and end up at a disadvantage when being ranked alongside popular products.
We propose a novel method for data curation that aggregates all customer engagements within a day for the same query to use as input training data.
- Score: 0.571097144710995
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: E-commerce websites use machine learned ranking models to serve shopping
results to customers. Typically, the websites log the customer search events,
which include the query entered and the resulting engagement with the shopping
results, such as clicks and purchases. Each customer search event serves as
input training data for the models, and the individual customer engagement
serves as a signal for customer preference. So a purchased shopping result, for
example, is perceived to be more important than one that is not. However, new
or under-impressed products do not have enough customer engagement signals and
end up at a disadvantage when being ranked alongside popular products. In this
paper, we propose a novel method for data curation that aggregates all customer
engagements within a day for the same query to use as input training data. This
aggregated customer engagement gives the models a complete picture of the
relative importance of shopping results. Training models on this aggregated
data leads to less reliance on behavioral features. This helps mitigate the
cold start problem and boosted relevant new products to top search results. In
this paper, we present the offline and online analysis and results comparing
the individual and aggregated customer engagement models trained on e-commerce
data.
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