Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we introduce A2G-DiffRec, a diffusion recommender that incorporates adaptive autoguidance, where the main model is guided by a less-trained version of itself. Instead of using a fixed guidance weight, A2G-DiffRec learns to adaptively weigh the outputs of the main and weak models during training, supervised by a fairness-aware regularization that promotes balanced exposure across items with different popularity levels. Experimental results on three public datasets show that A2G-DiffRec is effective in enhancing item-side fairness at a marginal cost of accuracy reduction compared to existing guided diffusion recommenders and other non-diffusion baselines.

Music is characterized by aspects related to different modalities, such as the audio signal, the lyrics, or the music video clips. This has motivated the development of multimodal datasets and methods for Music Information Retrieval (MIR) tasks such as genre classification or autotagging. Music can be described at different levels of granularity, for instance defining genres at the level of artists or music albums. However, most datasets for multimodal MIR neglect this aspect and provide data at the level of individual music tracks. We aim to fill this gap by providing Music4All Artist and Album (Music4All A+A), a dataset for multimodal MIR tasks based on music artists and albums. Music4All A+A is built on top of the Music4All-Onion dataset, an existing track-level dataset for MIR tasks. Music4All A+A provides metadata, genre labels, image repre-sentations, and textual descriptors for 6,741 artists and 19,511 albums. Furthermore, since Music4All A+A is built on top of Music4AII-Onion, it allows access to other multimodal data at the track level, including user-item interaction data. This renders Music4All A+A suitable for a broad range of MIR tasks, including multimodal music recommendation, at several levels of granularity. To showcase the use of Music4All A+A, we carry out experiments on multimodal genre classification of artists and albums, including an analysis in missing-modality scenarios, and a quantitative comparison with genre classification in the movie domain. Our experiments show that images are more informative for classifying the genres of artists and albums, and that several multimodal models for genre classification struggle in generalizing across domains. We provide the code to reproduce our experiments at https://github.com/hcai-mmslMusic4AlI-A-A, the dataset will be linked in the repository at camera-ready and provided open-source under a CC BY-NC-SA 4.0 license.

Recommender Systems (RS) often rely on representations of users and items in a joint embedding space and on a similarity metric to compute relevance scores.In modern RS, the modules to obtain user and item representations consist of two distinct and separate neural networks (NN).In multimodal representation learning, weight sharing has been proven effective in reducing the distance between multiple modalities of a same item.Inspired by these approaches, we propose a novel RS that leverages weight sharing between the user and item NN modules used to obtain the latent representations in the shared embedding space.The proposed framework consists of a single Collaborative Branch for Recommendation (CoBraR).We evaluate CoBraR by means of quantitative experiments on ecommerce and movie recommendation.Our experiments show that by reducing the number of parameters and improving beyondaccuracy aspects without compromising accuracy, CoBraR has the potential to be applied and extended for real-world scenarios.

Humans have the tendency to discover and explore.This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users.Additionally, in domains such as that of music streaming there is evidence that recommending novel content improves users' experience with the platform.Therefore, understanding users' discovery patterns, such as the amount to which and the way users access previously unknown content, is a topic of relevance for both the scientific community and the streaming industry, particularly the music one.Previous works studied how music consumption differs for users of different traits and looked at diversity, novelty, and consistency over time of users' music preferences.However, very little is known about how users discover and explore previously unknown music, and how this behavior differs for users of varying discovery needs.In this paper we bridge this gap by analyzing data from a survey answered by users of the major music streaming platform Deezer in combination with their streaming data.We first address questions regarding whether users who declare a higher interest in unfamiliar music listen to more diverse music, have more stable music preferences over time, and explore more music within a same time window, compared to those who declare a lower interest.We then investigate which type of music tracks users choose to listen to when they explore unfamiliar music, identifying clear patterns of popularity and genre representativeness that vary for users of different discovery needs.Our findings open up possibilities to infer users' interest in unfamiliar music from streaming data as well as possibilities to develop recommender systems that guide users in exploring music in a more natural way .

Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades when few or no interactions are available, a scenario referred to as cold-start. To address this issue, previous work relies on models leveraging both collaborative data and side information on the users or items. Similar to multimodal learning, these models aim at combining collaborative and content representations in a shared embedding space. In this work we propose a novel technique for multimodal recommendation, relying on a multimodal Single-Branch embedding network for Recommendation (SiBraR). Leveraging weight-sharing, SiBraR encodes interaction data as well as multimodal side information using the same single-branch embedding network on different modalities. This makes SiBraR effective in scenarios of missing modality, including cold start. Our extensive experiments on large-scale recommendation datasets from three different recommendation domains (music, movie, and e-commerce) and providing multimodal content information (audio, text, image, labels, and interactions) show that SiBraR significantly outperforms CF as well as state-of-the-art content-based RSs in cold-start scenarios, and is competitive in warm scenarios. We show that SiBraR’s recommendations are accurate in missing modality scenarios, and that the model is able to map different modalities to the same region of the shared embedding space, hence reducing the modality gap.

Recommender systems (RS) traditionally leverage the large amount of user–item interaction data. This exposes RS to a lower recommendation quality in cold-start scenarios, as well as to a low recommendation quality in terms of beyond-accuracy evaluation metrics. State-of-the-art (SotA) models for cold-start scenarios rely on the use of side information on the items or the users, therefore relating recommendation to multimodal machine learning (ML). However, the most recent techniques from multimodal ML are often not applied to the domain of recommendation. Additionally, the evaluation of SotA multimodal RS often neglects beyond-accuracy aspects of recommendation. In this work, we outline research into designing novel multimodal RS based on SotA multimodal ML architectures for cold-start recommendation, and their evaluation and benchmark with preexisting multimodal RS in terms of accuracy and beyond-accuracy aspects of recommendation quality.

Emotions constitute an important aspect when listening to music. While manual annotations from user studies grounded in psychological research on music and emotions provide a well-defined and fine-grained description of the emotions evoked when listening to a music track, user-generated tags provide an alternative view stemming from large-scale data. In this work, we examine the relationship between these two emotional characterizations of music and analyze their impact on the performance of emotion-based music recommender systems individually and jointly. Our analysis shows that (i) the agreement between the two characterizations, as measured with Cohen’s κ coefficient and Kendall rank correlation, is often low, (ii) Leveraging the emotion profile based on the intensity of evoked emotions from high-quality annotations leads to performances that are stable across different recommendation algorithms; (iii) Simultaneously leveraging the emotion profiles based on high-quality and large-scale annotations allows to provide recommendations that are less exposed to the low accuracy that algorithms might reach when leveraging one type of data, only.

The Psychology-informed Information Access Systems (PsyIAS) workshop bridges the fields of machine learning and psychology, aiming to connect the research communities of information retrieval, recommender systems, natural language processing, as well as cognitive and behavioral psychology. It serves as a forum for multidisciplinary discussions about the use of psychological constructs, theories, and empirical findings for modeling and predicting user preferences, intents, and behaviors. PsyIAS particularly focuses on research that incorporates such psychology-inspired models into the search, retrieval, and recommendation processes, creates corresponding algorithms and systems, or looks into the role of cognitive processes underlying human information access. More information can be found at https://sites.google.com/view/psyias.

Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.

When we appreciate a piece of music, it is most naturally because of its content, including rhythmic, tonal, and timbral elements as well as its lyrics and semantics. This suggests that the human affinity for music is inherently content-driven. This kind of information is, however, still frequently neglected by mainstream recommendation models based on collaborative filtering that rely solely on user-item interactions to recommend items to users. A major reason for this neglect is the lack of standardized datasets that provide both collaborative and content information. The work at hand addresses this shortcoming by introducing Music4All-Onion, a large-scale, multi-modal music dataset. The dataset expands the Music4All dataset by including 26 additional audio, video, and metadata characteristics for 109,269 music pieces. In addition, it provides a set of 252,984,396 listening records of 119,140 users, extracted from the online music platform Last.fm, which allows leveraging user-item interactions as well. We organize distinct item content features in an onion model according to their semantics, and perform a comprehensive examination of the impact of different layers of this model (e.g., audio features, user-generated content, and derivative content) on content-driven music recommendation, demonstrating how various content features influence accuracy, novelty, and fairness of music recommendation systems. In summary, with Music4All-Onion, we seek to bridge the gap between collaborative filtering music recommender systems and content-centric music recommendation requirements.