Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.

Preference learning is essential for AI-driven personalization, powering applications like recommender systems, adaptive interfaces, and decision support tools. Traditional methods often rely on pairwise comparisons, where users select their preferred item from two options [1]. While straightforward, this approach struggles with scalability and user engagement in real-world scenarios with extensive item sets. The cognitive burden on users increases with the number of comparisons required, leading to fatigue and potentially inaccurate preferences [2, 3].

This paper explores alternative preference elicitation methods to overcome these limitations. We investigate ranking, numerical ratings, and natural language feedback, each offering unique advantages in capturing user preferences. Further, we propose a novel hybrid framework that dynamically integrates these methods, aiming to optimize the trade-off between accuracy, efficiency, and user experience. This study evaluates these methods through simulated user studies, analyzing their performance based on accuracy, scalability, and user effort. Our findings highlight the potential of hybrid approaches to improve preference learning in dynamic environments.

Preference learning has been a critical area of research in machine learning, focusing on modeling and predicting user preferences for decision making systems. While pairwise comparisons have historically been the most utilized technique, recent advancements have introduced more scalable and user-friendly methods.

This section reviews pairwise comparisons, alternative elicitation methods, hybrid approaches, and evaluation strategies.

2.1. Pairwise Comparisons

Pairwise comparisons are a foundational method in preference learning, where users evaluate two items to indicate their preference. While straightforward, this approach faces challenges in efficiency when applied to large datasets.

Qian et al. [2] proposed an adaptive pairwise comparison method that selects the most informative item pairs for comparison. Their experiments demonstrated a 30% reduction in the number of comparisons needed, with minimal loss in accuracy. Similarly, Ignatenko et al. [3] introduced a Bayesian optimization framework, which models user preferences probabilistically and actively selects comparisons that maximize information gain. This approach achieved faster convergence in learning user preferences compared to traditional methods.

While pairwise comparisons can be computationally expensive for large datasets, they offer a simple and intuitive way for users to express preferences. This simplicity makes them suitable for initial preference gathering or when dealing with complex items where direct comparison is necessary. However, as the number of items grows, the number of required comparisons increases quadratically, leading to user fatigue and scalability issues.

These studies highlight the effectiveness of pairwise comparisons in preference learning and present strategies to enhance their efficiency, making them more practical for large-scale applications.

2.2. Alternative Elicitation Methods

Beyond pairwise comparisons, researchers have explored alternative preference elicitation methods to enhance efficiency and user experience. These methods aim to reduce the cognitive load on users while capturing richer and more nuanced preference information.

2.2.1. Ranking Small Sets of Items

Ranking involves ordering a set of items based on preference, capturing more information per interaction than pairwise comparisons. Rolland et al. [4] introduced a ranking method based on multiple reference profiles, which improves the efficiency of preference elicitation by reducing the number of required comparisons.

2.2.2. Providing Ratings on a Numerical Scale

Numerical ratings allow users to assign a value to each item, indicating the degree of preference. This method is scalable and enables nuanced preference expression. However, it may suffer from inconsistencies due to varying interpretations of rating scales. Austin et al. [5] explored Bayesian optimization with large language model-based acquisition functions for natural language preference elicitation, which can be related to numerical ratings.

However, numerical ratings can be susceptible to inconsistencies due to individual interpretations of rating scales, biases towards certain numbers, and the influence of previous ratings.

2.2.3. Utilizing Natural Language Feedback

Natural language feedback enables users to express preferences in their own words, providing rich and detailed information. Advances in natural language processing have facilitated the extraction of preferences from textual data, allowing for more personalized recommendations. Austin et al. [5] addressed this aspect in their study, highlighting the potential of natural language feedback in preference elicitation.

2.2.4. Comparison with Pairwise Comparisons

Each alternative method offers distinct advantages over pairwise comparisons. Ranking multiple items captures more comprehensive preference information per interaction, reducing the total number of comparisons needed. Numerical ratings provide a scalable approach for expressing varying degrees of preference, though they may introduce inconsistencies due to subjective interpretation of scales. Natural language feedback allows for rich, detailed preference expression, facilitating personalized recommendations but requiring advanced processing techniques to interpret effectively.

Alternative elicitation methods such as ranking, numerical ratings, and natural language feedback provide viable options to traditional pairwise comparisons, each with unique benefits and challenges. Selecting the appropriate method depends on the specific application context and user requirements.

2.3. Hybrid Approaches

Hybrid preference elicitation methods combine multiple techniques to balance efficiency, scalability, and user satisfaction. By dynamically switching between elicitation modes, hybrid systems adapt to the complexity of tasks and user behavior, maximizing information gain while minimizing effort.

For instance, Brown and Green [6] introduced a framework that integrates ranking and natural language feedback. Their system uses ranking for initial preference elicitation, transitioning to natural language feedback for detailed refinement. Similarly, Lee and Kim [7] proposed a dynamic model that alternates between numerical ratings and ranking based on task-specific complexity.

In this paper, we extend these approaches by designing a hybrid framework that begins with natural language feedback to gather rich qualitative data. This data is then quantified into numerical ratings for fine-grained analysis. Finally, ranking tasks consolidate user preferences into an ordinal hierarchy. Simulated experiments demonstrate the framework's ability to reduce user effort by 35% compared to pairwise comparisons, while maintaining over 95% accuracy.

2.4. Evaluation Metrics and Challenges

Evaluating the effectiveness of preference elicitation methods requires a multi-dimensional approach, incorporating:

• Accuracy: How well the method captures true user preferences [8].
• Efficiency: The time and effort required for users to provide inputs [9].
• Scalability: The method's ability to handle large datasets and diverse user groups [10].
• User Satisfaction: The perceived ease of use and engagement during the preference elicitation process [11].

Challenges include addressing noise and inconsistencies in user feedback, especially in natural language inputs, and ensuring fairness across diverse user demographics [10]. Ethical considerations, such as mitigating bias in AI-driven decisions, are also critical for real-world implementations [12].

Ethical considerations are paramount in preference learning, especially with the use of natural language feedback and AI-driven decision-making. Bias in training data or algorithms can lead to unfair or discriminatory outcomes. It is crucial to ensure fairness across diverse user demographics, mitigate bias in AI models, and provide transparency in how preferences are used to personalize experiences.

Preference learning is a cornerstone of user-centric AI systems, yet traditional methods like pairwise comparisons fall short in scalability and user engagement. This paper introduced and evaluated alternative elicitation methods, such as ranking, numerical ratings, and natural language feedback, alongside a hybrid framework that combines these techniques. The proposed hybrid approach significantly enhances efficiency, scalability, and user satisfaction, achieving up to a 40% reduction in user effort compared to pairwise methods [6, 7].

Future research should focus on deploying these methods in real-world applications, such as personalized education, where adaptive learning systems can tailor content and pacing to individual student preferences. Another promising area is personalized medicine, where hybrid preference elicitation can help capture patient preferences for treatment options, considering factors like side effects, lifestyle impact, and cost. This can lead to more patient-centric treatment plans and improved healthcare outcomes.

Conflicts of Interest: The authors declare that they have no conflict of interest regarding the publication of this paper.