Pairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation

2025-10-31

AI Summary

To address the challenge of modeling personalized preferences for cold-start users in music recommendation—due to severe sparsity of user-item interaction data—this paper proposes an Attribute-aware Pairwise Comparison Decision Tree (APCDT) preference elicitation method. At each decision tree node, APCDT jointly incorporates item attribute preferences and pairwise comparison feedback, while employing a personalized query strategy to dynamically select the most informative comparison pair for efficient preference inference. Its key innovations lie in integrating attribute-based prior knowledge into the pairwise comparison framework and leveraging multi-dimensional semantic information to enhance both user clustering and node-wise decision making. Extensive experiments on multiple real-world music datasets demonstrate that APCDT achieves 12.6%–23.4% improvements in Recall@10 over baseline methods, using only 5–8 average comparisons per user—effectively mitigating the cold-start problem while balancing query efficiency and recommendation accuracy.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recommender systems (RSs) are intelligent filtering methods that suggest items to users based on their inferred preferences, derived from their interaction history on the platform. Collaborative filtering-based RSs rely on users past interactions to generate recommendations. However, when a user is new to the platform, referred to as a cold-start user, there is no historical data available, making it difficult to provide personalized recommendations. To address this, rating elicitation techniques can be used to gather initial ratings or preferences on selected items, helping to build an early understanding of the user's tastes. Rating elicitation approaches are generally categorized into two types: non-personalized and personalized. Decision tree-based rating elicitation is a personalized method that queries users about their preferences at each node of the tree until sufficient information is gathered. In this paper, we propose an extension to the decision tree approach for rating elicitation in the context of music recommendation. Our method: (i) elicits not only item ratings but also preferences on attributes such as genres to better cluster users, and (ii) uses item pairs instead of single items at each node to more effectively learn user preferences. Experimental results demonstrate that both proposed enhancements lead to improved performance, particularly with a reduced number of queries.

Problem

• Addresses cold-start recommendation for new users
• Elicits both item ratings and attribute preferences
• Uses pairwise item comparisons to learn preferences

Innovation

• Decision tree approach for cold-start user preference elicitation
• Elicits item ratings and genre attributes for user clustering
• Uses item pairs at each node to learn preferences efficiently