Nasl. z nasl. zaslona.

Nasl. z nasl. zaslona.

Abstract Modern music streaming services rely on recommender systems to help users navigate within their large collections. Collaborative filtering (CF) methods, that leverage past user–item interactions, have been most successful, but have various limitations, like performing poorly among sparsely connected items. Conversely, content-based models circumvent the data-sparsity issue by recommending based on item content alone, but have seen limited success. Recently, graph-based machine learning approaches have shown, in other domains, to be able to address the aforementioned issues. Graph neural networks (GNN) in particular promise to learn from both the complex relationships within a user interaction graph, as well as content to generate hybrid recommendations. Here, we propose a music recommender system using a state-of-the-art GNN, PinSage, and evaluate it on a novel Spotify dataset against traditional CF, graph-based CF and content-based methods on a related song prediction task, venturing beyond accuracy in our evaluation. Our experiments show that (i) our approach is among the top performers and stands out as the most well rounded compared to baselines, (ii) graph-based CF methods outperform matrix-based CF approaches, suggesting that user interaction data may be better represented as a graph and (iii) in our evaluation, CF methods do not exhibit a performance drop in the long tail, where the hybrid approach does not offer an advantage.

Users of a recommender system may be requested to express their preferences about items either with evaluations of items (e.g. a rating) or with comparisons of item pairs. In this work we focus on the acquisition of pairwise preferences in the music domain. Asking the user to explicitly compare music, i.e., which, among two listened tracks, is preferred, requires some user effort. We have therefore developed a novel approach for automatically extracting these preferences from the analysis of the facial expressions of the users while listening to the compared tracks. We have trained a predictor that infers user's pairwise preferences by using features extracted from these data. We show that the predictor performs better than a commonly used baseline, which leverages the user's listening duration of the tracks to infer pairwise preferences. Furthermore, we show that there are differences in the accuracy of the proposed method between users with different personalities and we have therefore adapted the trained model accordingly. Our work shows that by introducing a low user effort preference elicitation approach, which, however, requires to access information that may raise potential privacy issues (face expression), one can obtain good prediction accuracy of pairwise music preferences.

We present a research tool for user preference elicitation that collects both explicit user feedback and unobtrusively acquired facial expressions. The concrete implementation is a web-based user interface where the user is presented with two music excerpts. After listening to both, the user provides a pairwise score (i.e. which of the two items is preferred) for each pair of music excerpts. The novelty of the demo is the integration of the unobtrusive acquisition of facial expressions through the webcam. During the listening of the music excerpts, the system extracts features related to the facial expressions of the user several times per second. The interaction runs as a web application, which allows for a large-scale remote acquisition of emotional data. Up to now, such acquisitions were usually done in controlled environments with few subjects, hence being of little use for the recommender systems community.