  In the vast landscape of internet information, recommender systems (RecSys)
have become essential for guiding users through a sea of choices aligned with
their preferences. These systems have applications in diverse domains, such as
news feeds, game suggestions, and shopping recommendations. Personalization is
a key technique in RecSys, where modern methods leverage representation
learning to encode user/item interactions into embeddings, forming the
foundation for personalized recommendations. However, integrating information
from multiple sources to enhance recommendation performance remains
challenging. This paper introduces a novel approach named PMTRec, the first
personalized multi-task learning algorithm to obtain comprehensive user/item
embeddings from various information sources. Addressing challenges specific to
personalized RecSys, we develop modules to handle personalized task weights,
diverse task orientations, and variations in gradient magnitudes across tasks.
PMTRec dynamically adjusts task weights based on gradient norms for each
user/item, employs a Task Focusing module to align gradient combinations with
the main recommendation task, and uses a Gradient Magnitude Balancing module to
ensure balanced training across tasks. Through extensive experiments on three
real-world datasets with different scales, we demonstrate that PMTRec
significantly outperforms existing multi-task learning methods, showcasing its
effectiveness in achieving enhanced recommendation accuracy by leveraging
multiple tasks simultaneously. Our contributions open new avenues for advancing
personalized multi-task training in recommender systems.
