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

TL;DR

This paper introduces an enhanced decision tree-based preference elicitation method for cold-start music recommendation, which gathers attribute preferences and uses item pairs to improve recommendation accuracy with fewer queries.

Contribution

It extends existing decision tree methods by incorporating attribute preferences and item pairs, leading to more efficient preference elicitation for new users.

Findings

Improved recommendation performance with fewer queries.

Effective clustering of users based on attribute preferences.

Enhanced learning of user preferences through item pairs.

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.