Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

TL;DR

This paper introduces DIHN, a novel recommendation model designed for Trigger-Induced Recommendation scenarios, effectively capturing users' instant interests to improve click-through rate predictions in e-commerce.

Contribution

The paper proposes a new model, DIHN, with three components tailored for TIR, addressing the challenge of capturing instant user interests unlike traditional methods.

Findings

DIHN outperforms existing models in offline evaluations.

DIHN shows significant online performance improvements.

The model effectively highlights instant user interests.

Abstract

In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms. In this paper, we present a new recommendation problem, Trigger-Induced Recommendation (TIR), where users' instant interest can be explicitly induced with a trigger item and follow-up related target items are recommended accordingly. TIR has become ubiquitous and popular in e-commerce platforms. In this paper, we figure out that although existing recommendation models are effective in traditional recommendation scenarios by mining users' interests based on their massive historical behaviors, they are struggling in discovering users' instant interests in the TIR scenario due to the discrepancy between these scenarios, resulting in inferior performance. To tackle the problem, we propose a novel recommendation method named Deep Interest Highlight Network (DIHN) for Click-Through Rate (CTR) prediction in TIR scenarios. It has three main components including 1) User Intent Network (UIN), which responds to generate a precise probability score to predict user's intent on the trigger item; 2) Fusion Embedding Module (FEM), which adaptively fuses trigger item and target item embeddings based on the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM), which can effectively highlight users' instant interest from their behaviors based on the result of FEM. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of DIHN over state-of-the-art methods.