Capturing Popularity Trends: A Simplistic Non-Personalized Approach for
  Enhanced Item Recommendation
Jiazheng Jing Yinan Zhang Xin Zhou Zhiqi Shen
Recommender systems have been gaining increasing research attention over the
years. Most existing recommendation methods focus on capturing users'
personalized preferences through historical user-item interactions, which may
potentially violate user privacy. Additionally, these approaches often overlook
the significance of the temporal fluctuation in item popularity that can sway
users' decision-making. To bridge this gap, we propose Popularity-Aware
Recommender (PARE), which makes non-personalized recommendations by predicting
the items that will attain the highest popularity. PARE consists of four
modules, each focusing on a different aspect: popularity history, temporal
impact, periodic impact, and side information. Finally, an attention layer is
leveraged to fuse the outputs of four modules. To our knowledge, this is the
first work to explicitly model item popularity in recommendation systems.
Extensive experiments show that PARE performs on par or even better than
sophisticated state-of-the-art recommendation methods. Since PARE prioritizes
item popularity over personalized user preferences, it can enhance existing
recommendation methods as a complementary component. Our experiments
demonstrate that integrating PARE with existing recommendation methods
significantly surpasses the performance of standalone models, highlighting
PARE's potential as a complement to existing recommendation methods.
Furthermore, the simplicity of PARE makes it immensely practical for industrial
applications and a valuable baseline for future research.