Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams. We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positive-only user feedback, often known as binary ratings. Our results show that online bagging is able to improve accuracy up to 35 over the baseline, with small computational overhead.
Ensemble methods in machine learning are convenient techniques to improve the accuracy of algorithms. Typically, this is achieved by combining results from a number of weaker sub-models. Bagging @cite_1 , Boosting @cite_12 and Stacking @cite_11 are three well-known ensemble methods used with recommendation algorithms. Boosting is experimented in @cite_8 @cite_0 @cite_4 @cite_9 , bagging is studied also in @cite_4 @cite_9 , and stacking in @cite_2 . In all of these contributions, ensemble methods work with batch learning algorithms only.
@cite_4 @cite_8 @cite_9 @cite_1 @cite_0 @cite_2 @cite_12 @cite_11
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We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filtering (CF) algorithms, which include: SVD, Neighborhood Based Approaches, Restricted Boltzmann Machine, Asymmetric Factor Model and Global Effects. We show that linearly combining (blending) a set of CF algorithms increases the accuracy and outperforms any single CF algorithm. Furthermore, we show how to use ensemble methods for blending predictors in order to outperform a single blending algorithm. The dataset and the source code for the ensemble blending are available online. Personalised recommender systems are widely used information filtering for information retrieval, where matrix factorisation (MF) has become popular as a model-based approach to personalised recommendation. Classical MF methods, which directly approximate low rank factor matrices by minimising some rating prediction criteria, do not achieve a satisfiable performance for the task of top-N recommendation. In this paper, we propose a novel MF method, namely BoostMF, that formulates factorisation as a learning problem and integrates boosting into factorisation. Rather than using boosting as a wrapper, BoostMF directly learns latent factors that are optimised toward the top-N recommendation. The proposed method is evaluated against a set of state-of-the-art methods on three popular public benchmark datasets. The experimental results demonstrate that the proposed method achieves significant improvement over these baseline methods for the task of top-N recommendation. An essential goal of the present web engineering is the development of efficient and competitive applications. This objective can be achieved by building recommender systems endowed with suitable web mining algorithms. Multiclassifiers are reliable data mining models that have been hardly used in the web system area. The paper presents a comparative study among different simple classifiers and multiclassifiers using a dataset from MovieLens recommender system. The aim of the work is to identify when the use of multiclassifiers in this type of systems is efficient. Recommender systems provide consumers with ratings of items. These ratings are based on a set of ratings that were obtained from a wide scope of users. Predicting the ratings can be formulated as a regression problem. Ensemble regression methods are effective tools that improve the results of simple regression algorithms by iteratively applying the simple algorithm to a diverse set of inputs. The present paper describes a simple and effective ensemble regressor for the prediction of missing ratings in recommender systems. The ensemble method is an adaptation of the AdaBoost regression algorithm for recommendation tasks. In all iterations, interpolation weights for all nearest neighbors are simultaneously derived by minimizing the root mean squared error. From iteration to iteration instances that are hard to predict are reinforced by manipulating their weights in the goal function that needs to be minimized. The experimental evaluation demonstrates that the ensemble methodology significantly improves the predictive performance of single neighborhood-based collaborative filtering. In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that con- sistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a "pseudo-loss" which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's "bagging" method when used to aggregate various classifiers (including decision trees and single attribute- value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.