Would You Like Your Data to Be Trained? A User Controllable Recommendation Framework

Abstract

Recommender systems have a significant impact on various real-world applications, shaping people's daily lives and enhancing productivity. Traditional recommender models aim to collect extensive user information to accurately estimate user preferences. However, in practical scenarios, users may not want all their behaviors to be included in the model training process. This paper introduces a novel recommendation paradigm that allows users to indicate their ``willingness'' regarding which data should contribute to model training. The models are then optimized to maximize utility, which considers the trade-off between recommendation performance and respecting user preferences. The recommendation problem is formulated as a multiplayer game, with each user acting as a player and using a selection vector to indicate their willingness to include specific interacted items in training. To efficiently solve this game, an influence function-based model is proposed to approximate recommendation performances for different actions without re-optimizing the model. Furthermore, an enhanced model leveraging multiple anchor actions for the influence function is introduced to improve performance approximation accuracy. The convergence rate of the algorithm is theoretically analyzed, and the advantages of incorporating multiple anchor actions are demonstrated. Extensive experiments on both simulated and real-world datasets validate the effectiveness of the proposed models in balancing recommendation quality and user willingness. To promote this research direction, we have released our project at https://paitesanshi.github.io/IFRQE/.