Aggregated Customer Engagement Model

TL;DR

This paper introduces a novel data aggregation method for customer engagement signals in e-commerce search ranking, improving the ranking of new products and reducing reliance on behavioral features.

Contribution

The paper proposes aggregating customer engagement data within a day for each query, enhancing model training and addressing cold start issues in product ranking.

Findings

Aggregated engagement data improves ranking of new products.

Models trained on aggregated data perform better offline and online.

Reduced reliance on behavioral features in ranking models.

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.