Personalized recommendation with corrected similarity
This work addresses a specific bottleneck in similarity-based recommendation methods for real-world systems, representing an incremental improvement.
The paper tackled the problem of overestimated or underestimated similarity computations in personalized recommendation systems by introducing a corrected similarity based inference (CSI) method that leverages mutual correction of forward and backward similarity estimations, resulting in greater improvement over mainstream baselines in experiments on four benchmark datasets.
Personalized recommendation attracts a surge of interdisciplinary researches. Especially, similarity based methods in applications of real recommendation systems achieve great success. However, the computations of similarities are overestimated or underestimated outstandingly due to the defective strategy of unidirectional similarity estimation. In this paper, we solve this drawback by leveraging mutual correction of forward and backward similarity estimations, and propose a new personalized recommendation index, i.e., corrected similarity based inference (CSI). Through extensive experiments on four benchmark datasets, the results show a greater improvement of CSI in comparison with these mainstream baselines. And the detailed analysis is presented to unveil and understand the origin of such difference between CSI and mainstream indices.