Incorporating Recklessness to Collaborative Filtering based Recommender Systems

arXiv:2308.02058v2 Announce Type: replace-cross
Abstract: Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This leads to a significant drop in the novelty of these systems, since instead of recommending uncertain unusual items, they focus on predicting items with guaranteed success. In this paper, we propose the inclusion of a new term in the learning process of matrix …

abstract arxiv collaborative collaborative filtering coverage cs.ai cs.ir cs.lg decision filtering leads recommender systems reliability stat.ml systems type will