Solving the Cold Start Problem on One's Own as an End User via Preference Transfer
This addresses the cold start problem for end users in recommender systems, offering a novel user-centric solution rather than incremental improvements.
The paper tackles the cold start problem in recommender systems by enabling end users to solve it independently without service provider support, proposing the Pretender algorithm that optimizes item selection based on distribution distance minimization and demonstrates effectiveness in experiments.
We propose a new approach that enables end users to directly solve the cold start problem by themselves. The cold start problem is a common issue in recommender systems, and many methods have been proposed to address the problem on the service provider's side. However, when the service provider does not take action, users are left with poor recommendations and no means to improve their experience. We propose an algorithm, Pretender, that allows end users to proactively solve the cold start problem on their own. Pretender does not require any special support from the service provider and can be deployed independently by users. We formulate the problem as minimizing the distance between the source and target distributions and optimize item selection from the target service accordingly. Furthermore, we establish theoretical guarantees for Pretender based on a discrete quadrature problem. We conduct experiments on real-world datasets to demonstrate the effectiveness of Pretender.