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Collaborative Filtering with Recency-based Negative Feedback by João Vinagre; Alípio Mário Jorge; João Gama is a scholarly article available to read on EtoBox.

What is Collaborative Filtering with Recency-based Negative Feedback about?

Many online communities and services continuously generate data that can be used by recommender systems. When explicit ratings are not available, rating prediction algorithms are not directly applicable. Instead, data consists of positive-only user-item interactions, and the task is therefore not to predict ratings, but rather to predict good items to recommend -item prediction. One particular challenge of positive-only data is how to interpret absent user-item interactions. These can either be seen as negative or as unknown preferences. In this paper, we propose a recencybased scheme to perform negative preference imputation in an incremental matrix factorization algorithm designed for streaming data. Our results show that this approach substantially improves the accuracy of the baseline method, outperforming both classic and state-of-the-art algorithms.

Author

João Vinagre; Alípio Mário Jorge; João Gama

Publisher

ACM

Published

2015

Language

EN