293 research outputs found
A Distributed and Accountable Approach to Offline Recommender Systems Evaluation
Different software tools have been developed with the purpose of performing
offline evaluations of recommender systems. However, the results obtained with
these tools may be not directly comparable because of subtle differences in the
experimental protocols and metrics. Furthermore, it is difficult to analyze in
the same experimental conditions several algorithms without disclosing their
implementation details. For these reasons, we introduce RecLab, an open source
software for evaluating recommender systems in a distributed fashion. By
relying on consolidated web protocols, we created RESTful APIs for training and
querying recommenders remotely. In this way, it is possible to easily integrate
into the same toolkit algorithms realized with different technologies. In
details, the experimenter can perform an evaluation by simply visiting a web
interface provided by RecLab. The framework will then interact with all the
selected recommenders and it will compute and display a comprehensive set of
measures, each representing a different metric. The results of all experiments
are permanently stored and publicly available in order to support
accountability and comparative analyses.Comment: REVEAL 2018 Workshop on Offline Evaluation for Recommender System
Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems
Recommender systems, while transformative in online user experiences, have
raised concerns over potential provider-side fairness issues. These systems may
inadvertently favor popular items, thereby marginalizing less popular ones and
compromising provider fairness. While previous research has recognized
provider-side fairness issues, the investigation into how these biases affect
beyond-accuracy aspects of recommendation systems - such as diversity, novelty,
coverage, and serendipity - has been less emphasized. In this paper, we address
this gap by introducing a simple yet effective post-processing re-ranking model
that prioritizes provider fairness, while simultaneously maintaining user
relevance and recommendation quality. We then conduct an in-depth evaluation of
the model's impact on various aspects of recommendation quality across multiple
datasets. Specifically, we apply the post-processing algorithm to four distinct
recommendation models across four varied domain datasets, assessing the
improvement in each metric, encompassing both accuracy and beyond-accuracy
aspects. This comprehensive analysis allows us to gauge the effectiveness of
our approach in mitigating provider biases. Our findings underscore the
effectiveness of the adopted method in improving provider fairness and
recommendation quality. They also provide valuable insights into the trade-offs
involved in achieving fairness in recommender systems, contributing to a more
nuanced understanding of this complex issue.Comment: FAccTRec at RecSys 202
CHESTNUT: Improve serendipity in movie recommendation by an Information Theory-based collaborative filtering approach
The term serendipity has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT , a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous runtime system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness , and the results show that it is fast, scalable and improves serendip-ity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
A filter bubble refers to the phenomenon where Internet customization
effectively isolates individuals from diverse opinions or materials, resulting
in their exposure to only a select set of content. This can lead to the
reinforcement of existing attitudes, beliefs, or conditions. In this study, our
primary focus is to investigate the impact of filter bubbles in recommender
systems. This pioneering research aims to uncover the reasons behind this
problem, explore potential solutions, and propose an integrated tool to help
users avoid filter bubbles in recommender systems. To achieve this objective,
we conduct a systematic literature review on the topic of filter bubbles in
recommender systems. The reviewed articles are carefully analyzed and
classified, providing valuable insights that inform the development of an
integrated approach. Notably, our review reveals evidence of filter bubbles in
recommendation systems, highlighting several biases that contribute to their
existence. Moreover, we propose mechanisms to mitigate the impact of filter
bubbles and demonstrate that incorporating diversity into recommendations can
potentially help alleviate this issue. The findings of this timely review will
serve as a benchmark for researchers working in interdisciplinary fields such
as privacy, artificial intelligence ethics, and recommendation systems.
Furthermore, it will open new avenues for future research in related domains,
prompting further exploration and advancement in this critical area.Comment: 21 pages, 10 figures and 5 table
Relational social recommendation: Application to the academic domain
This paper outlines RSR, a relational social recommendation approach applied to a social graph comprised of relational entity profiles. RSR uses information extraction and learning methods to obtain relational facts about persons of interest from the Web, and generates an associative entity-relation social network from their extracted personal profiles. As a case study, we consider the task of peer recommendation at scientific conferences. Given a social graph of scholars, RSR employs graph similarity measures to rank conference participants by their relatedness to a user. Unlike other recommender systems that perform social rankings, RSR provides the user with detailed supporting explanations in the form of relational connecting paths. In a set of user studies, we collected feedbacks from participants onsite of scientific conferences, pertaining to RSR quality of recommendations and explanations. The feedbacks indicate that users appreciate and benefit from RSR explainability features. The feedbacks further indicate on recommendation serendipity using RSR, having it recommend persons of interest who are not apriori known to the user, oftentimes exposing surprising inter-personal associations. Finally, we outline and assess potential gains in recommendation relevance and serendipity using path-based relational learning within RSR
Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity, and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions
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