9 research outputs found
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems
Recommender systems have become important tools to support users in
identifying relevant content in an overloaded information space. To ease the
development of recommender systems, a number of recommender frameworks have
been proposed that serve a wide range of application domains. Our TagRec
framework is one of the few examples of an open-source framework tailored
towards developing and evaluating tag-based recommender systems. In this paper,
we present the current, updated state of TagRec, and we summarize and reflect
on four use cases that have been implemented with TagRec: (i) tag
recommendations, (ii) resource recommendations, (iii) recommendation
evaluation, and (iv) hashtag recommendations. To date, TagRec served the
development and/or evaluation process of tag-based recommender systems in two
large scale European research projects, which have been described in 17
research papers. Thus, we believe that this work is of interest for both
researchers and practitioners of tag-based recommender systems.Comment: https://github.com/learning-layers/TagRe
A Re-visit of the Popularity Baseline in Recommender Systems
Popularity is often included in experimental evaluation to provide a
reference performance for a recommendation task. To understand how popularity
baseline is defined and evaluated, we sample 12 papers from top-tier
conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits.
We note that the widely adopted MostPop baseline simply ranks items based on
the number of interactions in the training data. We argue that the current
evaluation of popularity (i) does not reflect the popular items at the time
when a user interacts with the system, and (ii) may recommend items released
after a user's last interaction with the system. On the widely used MovieLens
dataset, we show that the performance of popularity could be significantly
improved by 70% or more, if we consider the popular items at the time point
when a user interacts with the system. We further show that, on MovieLens
dataset, the users having lower tendencies on movies tend to follow the crowd
and rate more popular movies. Movie lovers who rate a large number of movies,
rate movies based on their own preferences and interests. Through this study,
we call for a re-visit of the popularity baseline in recommender system to
better reflect its effectiveness.Comment: Accepted by SIGIR202
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
In this paper, we introduce a psychology-inspired approach to model and
predict the music genre preferences of different groups of users by utilizing
human memory processes. These processes describe how humans access information
units in their memory by considering the factors of (i) past usage frequency,
(ii) past usage recency, and (iii) the current context. Using a publicly
available dataset of more than a billion music listening records shared on the
music streaming platform Last.fm, we find that our approach provides
significantly better prediction accuracy results than various baseline
algorithms for all evaluated user groups, i.e., (i) low-mainstream music
listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream
music listeners. Furthermore, our approach is based on a simple psychological
model, which contributes to the transparency and explainability of the
calculated predictions.Comment: Dominik Kowald and Elisabeth Lex contributed equally to this wor
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms
In this paper, we study the imbalance between current state-of-the-art tag
recommendation algorithms and the folksonomy structures of real-world social
tagging systems. While algorithms such as FolkRank are designed for dense
folksonomy structures, most social tagging systems exhibit a sparse nature. To
overcome this imbalance, we show that cognitive-inspired algorithms, which
model the tag vocabulary of a user in a cognitive-plausible way, can be
helpful. Our present approach does this via implementing the activation
equation of the cognitive architecture ACT-R, which determines the usefulness
of units in human memory (e.g., tags). In this sense, our long-term research
goal is to design hybrid recommendation approaches, which combine the
advantages of both worlds in order to adapt to the current setting (i.e.,
sparse vs. dense ones).Comment: Presented at the European Symposium for Computational Social Science
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
Music recommender systems have become an integral part of music streaming
services such as Spotify and Last.fm to assist users navigating the extensive
music collections offered by them. However, while music listeners interested in
mainstream music are traditionally served well by music recommender systems,
users interested in music beyond the mainstream (i.e., non-popular music)
rarely receive relevant recommendations. In this paper, we study the
characteristics of beyond-mainstream music and music listeners and analyze to
what extent these characteristics impact the quality of music recommendations
provided. Therefore, we create a novel dataset consisting of Last.fm listening
histories of several thousand beyond-mainstream music listeners, which we
enrich with additional metadata describing music tracks and music listeners.
Our analysis of this dataset shows four subgroups within the group of
beyond-mainstream music listeners that differ not only with respect to their
preferred music but also with their demographic characteristics. Furthermore,
we evaluate the quality of music recommendations that these subgroups are
provided with four different recommendation algorithms where we find
significant differences between the groups. Specifically, our results show a
positive correlation between a subgroup's openness towards music listened to by
members of other subgroups and recommendation accuracy. We believe that our
findings provide valuable insights for developing improved user models and
recommendation approaches to better serve beyond-mainstream music listeners.Comment: Accepted for publication in EPJ Data Science - link to published
version will be adde
RSE: um Framework para Avaliação de Desempenho de Sistemas de Recomendação.
Os sistemas de recomendação são filtros que sugerem produtos de interesse para seus clientes, podendo assim causar um grande impacto nas vendas. Atualmente existe uma variedade desses algoritmos, sendo importante escolher a opção mais adequada ao problema em questão. Isso, no entanto, não é uma tarefa trivial. Nesse contexto é proposto o SER (Recommender Systems Evaluator): um framework que realiza a avaliação de desempenho offline dos sistemas de recomendação. O uso da metodologia apropriada é fundamental ao fazer uma avaliação. No entanto isso é frequentemente negligenciado, levando a resultados inconsistentes. O RSE procura abstrair ao máximo a complexidade envolvida no processo, e se baseia em conceitos estatísticos para proporcionar conclusões mais robustas. Os estudos realizados comprovam a sua eficácia, mostrando inclusive que ele pode ser adaptado para ser usado em outros contextos além dos sistemas de recomendação
Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications
The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations