Tensor-based Collaborative Filtering With Smooth Ratings Scale
This addresses a specific issue in recommender systems for users and platforms, but appears incremental as it builds on existing collaborative filtering techniques.
The paper tackles the problem of systematic errors in collaborative filtering caused by discrepancies in users' rating perceptions, such as some users rarely giving 5 stars while others frequently do, by introducing a ratings' similarity matrix to offset these effects and improve recommendation quality.
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.