  Recommender systems (RSs) are software tools and algorithms developed to
alleviate the problem of information overload, which makes it difficult for a
user to make right decisions. Two main paradigms toward the recommendation
problem are collaborative filtering and content-based filtering, which try to
recommend the best items using ratings and content available. These methods
typically face infamous problems including cold-start, diversity, scalability,
and great computational expense. We argue that the uptake of deep learning and
reinforcement learning methods is also questionable due to their computational
complexities and uninterpretability. In this paper, we approach the
recommendation problem from a new prospective. We borrow ideas from cluster
head selection algorithms in wireless sensor networks and adapt them to the
recommendation problem. In particular, we propose Load Balanced Recommender
System (LBRS), which uses a probabilistic scheme for item recommendation.
Furthermore, we factor in the importance of items in the recommendation
process, which significantly improves the recommendation accuracy. We also
introduce a method that considers a heterogeneity among items, in order to
balance the similarity and diversity trade-off. Finally, we propose a new
metric for diversity, which emphasizes the importance of diversity not only
from an intra-list perspective, but also from a between-list point of view.
With experiments in a simulation study performed on RecSim, we show that LBRS
is effective and can outperform baseline methods.
