Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering

2025-05-27

AI Summary

Modeling multi-source implicit feedback (e.g., clicks, add-to-cart, purchases) suffers from two key challenges: (1) difficulty in capturing the inherent sequential structure of user engagement, and (2) fragmented embedding spaces across feedback types, leading to incomparable representations and inefficient retrieval. Method: This paper proposes Generalized Neural Ordinal Logistic Regression (GNOLR), a unified framework addressing these issues via three core components: (1) a shared implicit embedding space enabling both cross-feedback representation sharing and feedback-specific dependency modeling; (2) an embedded progressive preference mechanism that explicitly encodes structured ordinal constraints; and (3) a nested optimization strategy for joint learning of multi-feedback dependencies. Results: Evaluated on ten real-world datasets, GNOLR consistently outperforms state-of-the-art methods, achieving superior predictive accuracy, efficient retrieval, and architectural simplicity—effectively capturing ambiguous, nonlinear ordinal relationships among implicit feedback signals.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Embedding-based collaborative filtering, often coupled with nearest neighbor search, is widely deployed in large-scale recommender systems for personalized content selection. Modern systems leverage multiple implicit feedback signals (e.g., clicks, add to cart, purchases) to model user preferences comprehensively. However, prevailing approaches adopt a feedback-wise modeling paradigm, which (1) fails to capture the structured progression of user engagement entailed among different feedback and (2) embeds feedback-specific information into disjoint spaces, making representations incommensurable, increasing system complexity, and leading to suboptimal retrieval performance. A promising alternative is Ordinal Logistic Regression (OLR), which explicitly models discrete ordered relations. However, existing OLR-based recommendation models mainly focus on explicit feedback (e.g., movie ratings) and struggle with implicit, correlated feedback, where ordering is vague and non-linear. Moreover, standard OLR lacks flexibility in handling feedback-dependent covariates, resulting in suboptimal performance in real-world systems. To address these limitations, we propose Generalized Neural Ordinal Logistic Regression (GNOLR), which encodes multiple feature-feedback dependencies into a unified, structured embedding space and enforces feedback-specific dependency learning through a nested optimization framework. Thus, GNOLR enhances predictive accuracy, captures the progression of user engagement, and simplifies the retrieval process. We establish a theoretical comparison with existing paradigms, demonstrating how GNOLR avoids disjoint spaces while maintaining effectiveness. Extensive experiments on ten real-world datasets show that GNOLR significantly outperforms state-of-the-art methods in efficiency and adaptability.

Problem

• Captures structured progression of user engagement across feedback
• Unifies feedback-specific information into a single embedding space
• Improves handling of implicit, correlated feedback in recommendation systems

Innovation

• Unified embedding space for multiple feedback signals
• Nested optimization for feedback-specific dependency learning
• Generalized Neural Ordinal Logistic Regression model