Informing the design of group recommender systems

Herr, Sascha; Rösch, Andreas; Beckmann, Christoph; Gross, Tom

doi:10.1145/2212776.2223827

Cited by 10 publications

(2 citation statements)

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“…When modelling these systems, it is vital to consider the group dynamics relevant to the particular domain or application in which the system will be used. For example, in a collaborative work environment, factors such as the level of expertise and experience of group members [21], their communication and collaboration styles [34], and their ability to work together effectively [38] may be decisive in group model. In social settings, such as planning a meal or a movie night with friends, aspects such as the group members' social relationships [8,13], their shared interests and preferences [8], and their ability to compromise and make decisions together may be more relevant.…”

Section: Introductionmentioning

confidence: 99%

GMAP 2023: 2nd Workshop on Group Modeling, Adaptation and Personalization

Barile

Delić

Peška

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

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Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems' modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work.

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Section: Introductionmentioning

confidence: 99%

GMAP 2023: 2nd Workshop on Group Modeling, Adaptation and Personalization

Barile

Delić

Peška

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

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“….2 Tipos de gruposAntes de adentrarnos en los modelos, vamos a analizar los tipos de grupos que existen, esto es un punto clave para el diseño del sistema, su entrenamiento y su evaluación. Existen muchos factores sociales para la formación de grupos[51,74], en este apartado centraremos el análisis en 3 puntos de vista diferentes: su estabilidad, su homogeneidad y su tamaño.…”

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Recomendaci�n a grupos de usuarios usando t�cnicas de aprendizaje profundo

Due�as Ler�n

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Recommender systems play a fundamental role in the task of filtering and retrieving information. In a world with instant access to vast amounts of information and with the emerging ability of generative models to produce even more, this kind of system will become increasingly important. In addition, the social nature of human beings and the integration of the digital world into everyday life make it increasingly necessary that recommenders consider the group of users that will interact with the system. The main idea behind group recommender systems is to have systems that handle the preferences of a group, to assist them in decision-making and accessing relevant information. The usefulness of these systems is found in very varied domains: audiovisual content platforms, tourist attractions planning, restaurant selection, etc. These systems have been developed with diverse machine learning techniques. This thesis focuses on exploring what improvements models based on deep learning can bring to this task. This work includes a detailed review of the state of the art of recommender systems. This review leads us to analyze the advances that have occurred in the field, from initial memory-based methods to the latest models based on deep learning techniques. One of the main characteristics of these techniques is that they can capture complex patterns in large data sets. Taking this into account, the main hypothesis of the thesis is that they can improve the performance of current methods by providing recommendations that satisfy the group of users. To test the hypothesis, different architectures and forms of representing the user group have been designed. These architectures and representations have been evaluated in four datasets. These datasets have different characteristics in terms of size, granularity, dispersion, etc. The goal is to confront the systems with different situations and recommendation scenarios to ensure the robustness and generalization of the obtained results. The datasets used are two versions of MovieLens, FilmTrust, and MyAnimeList. For the evaluation of the proposed models, standard metrics have been adopted: absolute error, square error, and max error in the predictions related to the group ratings. The results of the experiments carried out show how deep-learning based techniques offer significant improvements in the field of making recommendations to groups of users. Furthermore, this work serves as a basis for future research aimed at integrating the latest models and techniques in the deep learning area. RESUMEN Los sistemas de recomendaci�n juegan un papel fundamental en la tarea de filtrado y recuperaci�n de informaci�n. En un mundo con acceso a grandes cantidades de informaci�n de forma instant�nea y con la incipiente capacidad de los modelos generativos para generar a�n m�s contenido, este tipo de sistemas cobrar�n cada vez mayor importancia. Adem�s, la naturaleza social del ser humano y la integraci�n del mundo digital en la vida cotidiana, hacen necesario que estos sistemas incorporen caracter�sticas para tener en cuenta al grupo de usuarios que interactuar� con los elementos del sistema. La idea principal detr�s de los sistemas de recomendaci�n para grupos es tener sistemas que manejen las preferencias de varios usuarios al mismo tiempo, para ayudarles en la toma de decisiones y el acceso a la informaci�n relevante. La utilidad de estos sistemas se encuentra en dominios muy variados: consumo de contenido audiovisual, visita a atracciones tur�sticas, restauraci�n, etc. Estos sistemas para grupos se han desarrollado con diversos modelos de aprendizaje autom�tico. Esta tesis se centra en explorar qu� mejoras pueden aportar a esta tarea los modelos basados en aprendizaje profundo. En este trabajo se puede encontrar una revisi�n exhaustiva del estado del arte de los sistemas de recomendaci�n. Esta revisi�n nos lleva a analizar los avances que han ocurrido en el campo, desde los iniciales m�todos basados en memoria, hasta los modelos basados en aprendizaje profundo. Estos �ltimos, se caracterizan por su capacidad de capturar patrones complejos en grandes conjuntos de datos. La hip�tesis principal de la tesis es que pueden mejorar a los m�todos actuales ofreciendo recomendaciones que satisfagan al grupo de usuarios. Para comprobar la hip�tesis, se han dise�ado distintas formas de representaci�n de grupos de usuarios y distintas arquitecturas profundas. Estas representaciones y arquitecturas se han evaluado en cuatro datasets. Estos datasets tienen distintas caracter�sticas en cuanto a tama�o, granularidad, dispersi�n, etc. El objetivo es enfrentar a los sistemas a distintas situaciones y escenarios de recomendaci�n para asegurar la robustez y generalizaci�n de los resultados obtenidos. Los datasets que se han utilizado son: dos versiones de MovieLens, FilmTrust y MyAnimeList. Para la evaluaci�n de los modelos propuestos, se han adoptado m�tricas est�ndar en el campo de los sistemas de recomendaci�n: error absoluto, error cuadr�tico y error m�ximo cometido en las predicciones respecto al grupo de usuarios. Los resultados de los experimentos llevados a cabo muestran c�mo las t�cnicas basadas en aprendizaje profundo ofrecen mejoras significativas dentro del problema de realizar recomendaciones a grupos de usuarios. Adem�s, este trabajo sirve de base para realizar futuras investigaciones mediante la incorporaci�n de los �ltimos avances en el �rea del aprendizaje profundo.

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Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

Masthoff

2015

Recommender Systems Handbook

103

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Informing the design of group recommender systems

Cited by 10 publications

References 12 publications

GMAP 2023: 2nd Workshop on Group Modeling, Adaptation and Personalization

GMAP 2023: 2nd Workshop on Group Modeling, Adaptation and Personalization

Recomendaci�n a grupos de usuarios usando t�cnicas de aprendizaje profundo

Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

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