18,430 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Co-evolving Vector Quantization for ID-based Recommendation
Category information plays a crucial role in enhancing the quality and
personalization of recommendations. Nevertheless, the availability of item
category information is not consistently present, particularly in the context
of ID-based recommendations. In this work, we propose an alternative approach
to automatically learn and generate entity (i.e., user and item) categorical
information at different levels of granularity, specifically for ID-based
recommendation. Specifically, we devise a co-evolving vector quantization
framework, namely COVE, which enables the simultaneous learning and refinement
of code representation and entity embedding in an end-to-end manner, starting
from the randomly initialized states. With its high adaptability, COVE can be
easily integrated into existing recommendation models. We validate the
effectiveness of COVE on various recommendation tasks including list
completion, collaborative filtering, and click-through rate prediction, across
different recommendation models. We will publish the code and data for other
researchers to reproduce our work
INFACT: An Online Human Evaluation Framework for Conversational Recommendation
Conversational recommender systems (CRS) are interactive agents that support
their users in recommendation-related goals through multi-turn conversations.
Generally, a CRS can be evaluated in various dimensions. Today's CRS mainly
rely on offline(computational) measures to assess the performance of their
algorithms in comparison to different baselines. However, offline measures can
have limitations, for example, when the metrics for comparing a newly generated
response with a ground truth do not correlate with human perceptions, because
various alternative generated responses might be suitable too in a given dialog
situation. Current research on machine learning-based CRS models therefore
acknowledges the importance of humans in the evaluation process, knowing that
pure offline measures may not be sufficient in evaluating a highly interactive
system like a CRS.Comment: 6 pages, 2 figures
A hybrid approach for item collection recommendations : an application to automatic playlist continuation
Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction.
In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience.
Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Automatic bundle construction is a crucial prerequisite step in various
bundle-aware online services. Previous approaches are mostly designed to model
the bundling strategy of existing bundles. However, it is hard to acquire
large-scale well-curated bundle dataset, especially for those platforms that
have not offered bundle services before. Even for platforms with mature bundle
services, there are still many items that are included in few or even zero
bundles, which give rise to sparsity and cold-start challenges in the bundle
construction models. To tackle these issues, we target at leveraging multimodal
features, item-level user feedback signals, and the bundle composition
information, to achieve a comprehensive formulation of bundle construction.
Nevertheless, such formulation poses two new technical challenges: 1) how to
learn effective representations by optimally unifying multiple features, and 2)
how to address the problems of modality missing, noise, and sparsity problems
induced by the incomplete query bundles. In this work, to address these
technical challenges, we propose a Contrastive Learning-enhanced Hierarchical
Encoder method (CLHE). Specifically, we use self-attention modules to combine
the multimodal and multi-item features, and then leverage both item- and
bundle-level contrastive learning to enhance the representation learning, thus
to counter the modality missing, noise, and sparsity problems. Extensive
experiments on four datasets in two application domains demonstrate that our
method outperforms a list of SOTA methods. The code and dataset are available
at https://github.com/Xiaohao-Liu/CLHE
FMMRec: Fairness-aware Multimodal Recommendation
Recently, multimodal recommendations have gained increasing attention for
effectively addressing the data sparsity problem by incorporating
modality-based representations. Although multimodal recommendations excel in
accuracy, the introduction of different modalities (e.g., images, text, and
audio) may expose more users' sensitive information (e.g., gender and age) to
recommender systems, resulting in potentially more serious unfairness issues.
Despite many efforts on fairness, existing fairness-aware methods are either
incompatible with multimodal scenarios, or lead to suboptimal fairness
performance due to neglecting sensitive information of multimodal content. To
achieve counterfactual fairness in multimodal recommendations, we propose a
novel fairness-aware multimodal recommendation approach (dubbed as FMMRec) to
disentangle the sensitive and non-sensitive information from modal
representations and leverage the disentangled modal representations to guide
fairer representation learning. Specifically, we first disentangle biased and
filtered modal representations by maximizing and minimizing their sensitive
attribute prediction ability respectively. With the disentangled modal
representations, we mine the modality-based unfair and fair (corresponding to
biased and filtered) user-user structures for enhancing explicit user
representation with the biased and filtered neighbors from the corresponding
structures, followed by adversarially filtering out sensitive information.
Experiments on two real-world public datasets demonstrate the superiority of
our FMMRec relative to the state-of-the-art baselines. Our source code is
available at https://anonymous.4open.science/r/FMMRec
Automatically learning topics and difficulty levels of problems in online judge systems
Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation
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