Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks
This addresses a bias in recommender systems that affects users by improving cold-start recommendations without needing positive feedback, though it is incremental as it builds on existing tensor factorization methods.
The paper tackles the problem of standard collaborative filtering algorithms being insensitive to negative feedback in top-N recommendations by modeling user feedback as a categorical variable with ternary tensor factorization, achieving state-of-the-art quality in standard tasks and significantly outperforming others in cold-start scenarios with no positive feedback.
Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite - avoiding recommendations with completely irrelevant items. Due to that bias, standard algorithms, as well as commonly used evaluation metrics, become insensitive to negative feedback. In order to resolve this problem we propose to treat user feedback as a categorical variable and model it with users and items in a ternary way. We employ a third-order tensor factorization technique and implement a higher order folding-in method to support online recommendations. The method is equally sensitive to entire spectrum of user ratings and is able to accurately predict relevant items even from a negative only feedback. Our method may partially eliminate the need for complicated rating elicitation process as it provides means for personalized recommendations from the very beginning of an interaction with a recommender system. We also propose a modification of standard metrics which helps to reveal unwanted biases and account for sensitivity to a negative feedback. Our model achieves state-of-the-art quality in standard recommendation tasks while significantly outperforming other methods in the cold-start "no-positive-feedback" scenarios.