NDLI: Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

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Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

Content Provider	ACM Digital Library
Author	Oseledets, Ivan Frolov, Evgeny
Abstract	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.
Starting Page	91
Ending Page	98
Page Count	8
File Format	PDF MP4
ISBN	9781450340359
DOI	10.1145/2959100.2959170
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Explicit feedback Collaborative filtering Tensor factorization Cold-start Recommender systems
Content Type	Audio Text
Resource Type	Article

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