GenCI: Generative Modeling of User Interest Shift via Cohort-based Intent Learning for CTR Prediction

TL;DR

GenCI introduces a generative framework that models dynamic user interests using semantic cohorts, improving CTR prediction by capturing immediate intent and contextual signals beyond traditional point-wise ranking methods.

Contribution

The paper proposes GenCI, a novel generative user intent model leveraging semantic cohorts and a hierarchical network, addressing interest shift and contextual information gaps in CTR prediction.

Findings

Outperforms existing models on three datasets.

Effectively captures immediate user intent.

Enhances CTR prediction accuracy.

Abstract

Click-through rate (CTR) prediction plays a pivotal role in online advertising and recommender systems. Despite notable progress in modeling user preferences from historical behaviors, two key challenges persist. First, exsiting discriminative paradigms focus on matching candidates to user history, often overfitting to historically dominant features and failing to adapt to rapid interest shifts. Second, a critical information chasm emerges from the point-wise ranking paradigm. By scoring each candidate in isolation, CTR models discard the rich contextual signal implied by the recalled set as a whole, leading to a misalignment where long-term preferences often override the user's immediate, evolving intent. To address these issues, we propose GenCI, a generative user intent framework that leverages semantic interest cohorts to model dynamic user preferences for CTR prediction. The framework first employs a generative model, trained with a next-item prediction (NTP) objective, to proactively produce candidate interest cohorts. These cohorts serve as explicit, candidate-agnostic representations of a user's immediate intent. A hierarchical candidate-aware network then injects this rich contextual signal into the ranking stage, refining them with cross-attention to align with both user history and the target item. The entire model is trained end-to-end, creating a more aligned and effective CTR prediction pipeline. Extensive experiments on three widely used datasets demonstrate the effectiveness of our approach.