Solving the Cold Start Problem on One's Own as an End User via Preference Transfer

TL;DR

This paper introduces Pretender, a user-centric algorithm that empowers end users to independently address the cold start problem in recommender systems by transferring preferences without service provider intervention.

Contribution

It presents a novel algorithm enabling users to solve cold start issues independently, with theoretical guarantees and practical effectiveness demonstrated through experiments.

Findings

Pretender effectively improves recommendations for new users.

Theoretical guarantees support the algorithm's reliability.

Experimental results show significant performance gains.

Abstract

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.