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

2026-01-26

AI Summary

This work addresses the limitations of existing CTR prediction methods, which are prone to overfitting dominant features in user history, struggle to capture rapidly evolving immediate intent, and employ pointwise ranking that neglects the global context of the retrieved candidate set—often causing long-term preferences to overshadow short-term interests. To overcome these issues, the authors propose GenCI, a novel framework that introduces a generative interest grouping mechanism to explicitly model candidate-agnostic immediate intent through a next-item prediction (NTP) objective. Furthermore, a hierarchical candidate-aware network is designed, leveraging cross-attention to inject group-level semantic context into the ranking stage. This enables end-to-end joint optimization of intent generation and ranking, transcending conventional discriminative pointwise paradigms. Extensive experiments on three mainstream datasets demonstrate significant CTR improvements, validating the framework’s effectiveness in dynamic interest modeling and contextual utilization of retrieval candidates.

Technology Category

Machine Learning: Learning Preferences or RankingsNatural Language Processing: GenerationData Mining & Knowledge Management: Recommender Systems

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingWeb Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• CTR prediction
• user interest shift
• contextual signal
• ranking paradigm
• generative modeling

Innovation

• generative modeling
• interest shift
• cohort-based intent learning
• CTR prediction
• contextual signal