Abstract
Machine Learning algorithms are often too complex to be studied from a purely analytical point of view. Alternatively, with a reasonably large number of datasets one can empirically observe the behavior of a given algorithm in different conditions and hypothesize some general characteristics. This knowledge about algorithms can be used to choose the most appropriate one given a new dataset. This very hard problem can be approached using metalearning. Unfortunately, the number of datasets available may not be sufficient to obtain reliable meta-knowledge. Additionally, datasets may change with time, by growing, shrinking and editing, due to natural actions like people buying in a e-commerce site. In this paper we propose dataset morphing as the basis of a novel methodology that can help overcome these drawbacks and can be used to better understand ML algorithms. It consists of manipulating real datasets through the iterative application of gradual transformations (morphing) and by observing the changes in the behavior of learning algorithms while relating these changes with changes in the meta features of the morphed datasets. Although dataset morphing can be envisaged in a much wider framework, we focus on one very specific instance: the study of collaborative filtering algorithms on binary data. Results show that the proposed approach is feasible and that it can be used to identify useful metafeatures to predict the best collaborative filtering algorithm for a given dataset.

Abstract
The present paper sets a milestone on incremental recommender systems approaches by comparing several state-of-the-art algorithms with two different mathematical foundations - matrix and tensor factorization. Traditional Pairwise Interaction Tensor Factorization is revisited and converted into a scalable and incremental option that yields the best predictive power. A novel tensor inspired approach is described. Finally, experiments compare contextless vs context-aware scenarios, the impact of noise on the algorithms, discrepancies between time complexity and execution times, and are run on five different datasets from three different recommendation areas - music, gross retail and garment. Relevant conclusions are drawn that aim to help choosing the most appropriate algorithm to use when faced with a novel recommender tasks.

Abstract

Abstract

Abstract
Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content - e.g. posts, news, products, comments -, but also user feedback - e.g. ratings, views, reads, clicks, thumbs up -, as well as context information - device used, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, as well as other related tasks, such as evaluation, reproducibility, privacy and explainability.

Abstract
Online recommender systems often deal with continuous, potentially fast and unbounded ows 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 are capable of processing those data streams on the y. We propose online bagging, using an incremental matrix factorization algorithm for positiveonly data streams. Using prequential evaluation, we show that bagging is able to improve accuracy more than 20% over the baseline with small computational overhead.