1,240 research outputs found
Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
The evaluation of recommendation systems is a complex task. The offline and
online evaluation metrics for recommender systems are ambiguous in their true
objectives. The majority of recently published papers benchmark their methods
using ill-posed offline evaluation methodology that often fails to predict true
online performance. Because of this, the impact that academic research has on
the industry is reduced. The aim of our research is to investigate and compare
the online performance of offline evaluation metrics. We show that penalizing
popular items and considering the time of transactions during the evaluation
significantly improves our ability to choose the best recommendation model for
a live recommender system. Our results, averaged over five large-size
real-world live data procured from recommenders, aim to help the academic
community to understand better offline evaluation and optimization criteria
that are more relevant for real applications of recommender systems.Comment: Accepted to evalRS 2023@KD
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Wavelet-Based Cancer Drug Recommender System
A natureza molecular do cancro serve de base para estudos sistemáticos de genomas
cancerígenos, fornecendo valiosos insights e permitindo o desenvolvimento de
tratamentos clínicos. Acima de tudo, estes estudos estão a impulsionar o uso clínico de
informação genómica na escolha de tratamentos, de outro modo não expectáveis, em
pacientes com diversos tipos de cancro, possibilitando a medicina de precisão.
Com isso em mente, neste projeto combinamos técnicas de processamento de imagem,
para aprimoramento de dados, e sistemas de recomendação para propor um ranking
personalizado de drogas anticancerígenas. O sistema é implementado em Python e testado
usando uma base de dados que contém registos de sensibilidade a drogas, com mais de
310.000 IC50 que, por sua vez, descrevem a resposta de mais de 300 drogas
anticancerígenas em 987 linhas celulares cancerígenas.
Após várias tarefas de pré-processamento, são realizadas duas experiências. A primeira
experiência usa as imagens originais de microarrays de DNA e a segunda usa as mesmas
imagens, mas submetidas a uma transformada wavelet. As experiências confirmam que
as imagens de microarrays de DNA submetidas a transformadas wavelet melhoram o
desempenho do sistema de recomendação, otimizando a pesquisa de linhas celulares
cancerígenas com perfil semelhante ao da nova linha celular.
Além disso, concluímos que as imagens de microarrays de DNA com transformadas de
wavelet apropriadas, não apenas fornecem informações mais ricas para a pesquisa de
utilizadores similares, mas também comprimem essas imagens com eficiência,
otimizando os recursos computacionais.
Tanto quanto é do nosso conhecimento, este projeto é inovador no que diz respeito ao uso
de imagens de microarrays de DNA submetidas a transformadas wavelet, para perfilar
linhas celulares num sistema de recomendação personalizado de drogas anticancerígenas
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform
Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation
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