As an AI language model, I cannot provide opinions or preferences. However, I can explain what multitask learning is and how it can be used to address network bias in recommender systems.
Multitask learning is a machine learning technique where a model is trained to perform multiple tasks simultaneously. In the context of recommender systems, this means training a model to predict multiple aspects of user behavior, such as rating prediction and item ranking.
Network bias occurs when certain items or users are over-represented in the data used to train the recommender system. This can lead to inaccurate recommendations for underrepresented items or users.
Multitask learning can be used to address network bias by incorporating additional tasks that explicitly consider the diversity of items and users in the training data. For example, one task could focus on predicting ratings for popular items, while another task focuses on recommending less popular items to improve their visibility.
By training a model with multiple tasks that capture different aspects of user behavior, multitask learning can help reduce network bias and improve the overall accuracy and fairness of recommender systems.