TL;DR
DPAN is a novel neural network model that dynamically learns fine-grained similarity and diversity preferences for relevant e-commerce recommendations, significantly improving click-through rates.
Contribution
The paper introduces DPAN, a new method that captures users' dynamic preferences for similarity and diversity at a fine-grained level in relevant recommendations.
Findings
7.62% CTR improvement in offline experiments
Successful deployment on an e-commerce platform
Code is publicly available
Abstract
In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in. However, users' preferences for the similarity and diversity of recommendation results are dynamic and vary under different conditions. Moreover, individual item-level diversity is too coarse-grained since all recommended items are related to the trigger item. Thus, the two main challenges are to learn fine-grained representations of similarity and diversity and capture users' dynamic preferences for them under different conditions. To address these challenges, we propose a novel method called the Dynamic Preference-based and Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in relevant recommendations. Specifically, based on Attribute-aware Activation Values Generation (AAVG), Bi-dimensional Compression-based…
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