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Publications

Publications by João Vinagre

2015

Collaborative filtering with recency-based negative feedback

Authors
Vinagre, J; Jorge, AM; Gama, J;

Publication
30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II

Abstract
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 recency-based 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.

2015

Evaluation of recommender systems in streaming environments

Authors
Vinagre, Joao; Jorge, AlipioMario; Gama, Joao;

Publication
CoRR

Abstract

2015

Forgetting Methods for Incremental Matrix Factorization in Recommender Systems

Authors
Matuszyk, P; Vinagre, J; Spiliopoulou, M; Jorge, AM; Gama, J;

Publication
30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II

Abstract
Numerous stream mining algorithms are equipped with forgetting mechanisms, such as sliding windows or fading factors, to make them adaptive to changes. In recommender systems those techniques have not been investigated thoroughly despite the very volatile nature of users' preferences that they deal with. We developed five new forgetting techniques for incremental matrix factorization in recommender systems. We show on eight datasets that our techniques improve the predictive power of recommender systems. Experiments with both explicit rating feedback and positive-only feedback confirm our findings showing that forgetting information is beneficial despite the extreme data sparsity that recommender systems struggle with. Improvement through forgetting also proves that users' preferences are subject to concept drift.

2013

Combining usage and content in an online recommendation system for music in the Long Tail

Authors
Domingues, MA; Gouyon, F; Jorge, AM; Leal, JP; Vinagre, J; Lemos, L; Sordo, M;

Publication
IJMIR

Abstract

2014

Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

Authors
Vinagre, J; Jorge, AM; Gama, J;

Publication
USER MODELING, ADAPTATION, AND PERSONALIZATION, UMAP 2014

Abstract
Traditional Collaborative Filtering algorithms for recommendation are designed for stationary data. Likewise, conventional evaluation methodologies are only applicable in offline experiments, where data and models are static. However, in real world systems, user feedback is continuously being generated, at unpredictable rates. One way to deal with this data stream is to perform online model updates as new data points become available. This requires algorithms able to process data at least as fast as it is generated. One other issue is how to evaluate algorithms in such a streaming data environment. In this paper we introduce a simple but fast incremental Matrix Factorization algorithm for positive-only feedback. We also contribute with a prequential evaluation protocol for recommender systems, suitable for streaming data environments. Using this evaluation methodology, we compare our algorithm with other state-of-the-art proposals. Our experiments reveal that despite its simplicity, our algorithm has competitive accuracy, while being significantly faster.

2014

Monitoring Recommender Systems: A Business Intelligence Approach

Authors
Felix, C; Soares, C; Jorge, A; Vinagre, J;

Publication
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, PART VI - ICCSA 2014

Abstract
Recommender systems (RS) are increasingly adopted by e-business, social networks and many other user-centric websites. Based on the user's previous choices or interests, a RS suggests new items in which the user might be interested. With constant changes in user behavior, the quality of a RS may decrease over time. Therefore, we need to monitor the performance of the RS, giving timely information to management, who can than manage the RS to maximize results. Our work consists in creating a monitoring platform - based on Business Intelligence (BI) and On-line Analytical Processing (OLAP) tools - that provides information about the recommender system, in order to assess its quality, the impact it has on users and their adherence to the recommendations. We present a case study with Palco Principal(1), a social network for music.

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