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Personalization and context-awareness are highly important topics in research on Intelligent Information Systems. In the fields of Music Information Retrieval (MIR) and Music Recommendation in particular, user-centric algorithms should ideally provide music that perfectly fits each individual listener in each imaginable situation and for each of her information or entertainment needs. Even though preliminary steps towards such systems have recently been presented at the "International Society for Music Information Retrieval Conference" (ISMIR) and at similar venues, this vision is still far away from becoming a reality. In this article, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking computer science and psychology points of view. Whereas the computer science aspect centers on the problems of user modeling, machine learning,

The field of Music Information Retrieval has always acknowledged the need for rigorous scientific evaluations, and several efforts have set out to develop and provide the infrastructure, technology and methodologies needed to carry out these evaluations. The community has enormously gained from these evaluation forums, but we have reached a point where we are stuck with evaluation frameworks that do not allow us to improve as much and as well as we want. The community recently acknowledged this problem and showed interest in addressing it, though it is not clear what to do to improve the situation. We argue that a good place to start is again the Text IR field. Based on a formalization of the evaluation process, this paper presents a survey of past evaluation work in the context of Text IR, from the point of view of validity, reliability and efficiency of the experiments. We show the problems that our community currently has in terms of evaluation, point to several lines of research to improve it and make various proposals in that line.

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Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.

Previous research has suggested the permutation test as the theoretically optimal statistical significance test for IR evaluation, and advocated for the discontinuation of the Wilcoxon and sign tests. We present a large-scale study comprising nearly 60 million system comparisons showing that in practice the bootstrap, t-test and Wilcoxon test outperform the permutation test under different optimality criteria. We also show that actual error rates seem to be lower than the theoretically expected 5%, further confirming that we may actually be underestimating significance.

The number of topics that a test collection contains has a direct impact on how well the evaluation results reflect the true performance of systems. However, large collections can be prohibitively expensive, so researchers are bound to balance reliability and cost. This issue arises when researchers have an existing collection and they would like to know how much they can trust their results, and also when they are building a new collection and they would like to know how many topics it should contain before they can trust the results. Several measures have been proposed in the literature to quantify the accuracy of a collection to estimate the true scores, as well as different ways to estimate the expected accuracy of hypothetical collections with a certain number of topics. We can find ad-hoc measures such as Kendall tau correlation and swap rates, and statistical measures such as statistical power and indexes from generalizability theory. Each measure focuses on different aspects of evaluation, has a different theoretical basis, and makes a number of assumptions that are not met in practice, such as normality of distributions, homoscedasticity, uncorrelated effects and random sampling. However, how good these estimates are in practice remains a largely open question.In this paper we first compare measures and estimators of test collection accuracy and propose unbiased statistical estimators of the Kendall tau and tau AP correlation coefficients. Second, we detail a method for stochastic simulation of evaluation results under different statistical assumptions, which can be used for a variety of evaluation research where we need to know the true scores of systems.