196 research outputs found
Recommender systems fairness evaluation via generalized cross entropy
Fairness in recommender systems has been considered with respect
to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue
in a multistakeholder setting). Regardless, the concept has been
commonly interpreted as some form of equality – i.e., the degree to
which the system is meeting the information needs of all its users in
an equal sense. In this paper, we argue that fairness in recommender
systems does not necessarily imply equality, but instead it should
consider a distribution of resources based on merits and needs.We
present a probabilistic framework based ongeneralized cross entropy
to evaluate fairness of recommender systems under this perspective,
wherewe showthat the proposed framework is flexible and explanatory
by allowing to incorporate domain knowledge (through an ideal
fair distribution) that can help to understand which item or user aspects
a recommendation algorithm is over- or under-representing.
Results on two real-world datasets show the merits of the proposed
evaluation framework both in terms of user and item fairnessThis work was supported in part by the Center for Intelligent Information
Retrieval and in part by project TIN2016-80630-P (MINECO
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
In multimodal-aware recommendation, the extraction of meaningful multimodal
features is at the basis of high-quality recommendations. Generally, each
recommendation framework implements its multimodal extraction procedures with
specific strategies and tools. This is limiting for two reasons: (i) different
extraction strategies do not ease the interdependence among multimodal
recommendation frameworks; thus, they cannot be efficiently and fairly
compared; (ii) given the large plethora of pre-trained deep learning models
made available by different open source tools, model designers do not have
access to shared interfaces to extract features. Motivated by the outlined
aspects, we propose Ducho, a unified framework for the extraction of multimodal
features in recommendation. By integrating three widely-adopted deep learning
libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we
provide a shared interface to extract and process features where each backend's
specific methods are abstracted to the end user. Noteworthy, the extraction
pipeline is easily configurable with a YAML-based file where the user can
specify, for each modality, the list of models (and their specific
backends/parameters) to perform the extraction. Finally, to make Ducho
accessible to the community, we build a public Docker image equipped with a
ready-to-use CUDA environment and propose three demos to test its
functionalities for different scenarios and tasks. The GitHub repository and
the documentation is accessible at this link:
https://github.com/sisinflab/Ducho
PrOnto: an Ontology Driven Business Process Mining Tool
Abstract The main aim of data mining techniques and tools is that of identify and extract, from a set of (big) data, implicit patterns which can describe static or dynamic phenomena. Among these latter business processes are gaining more and more attention due to their crucial role in modern organizations and enterprises. Being able to identify and model processes inside organizations is for sure a key asset to discover their weak and strong points thus helping them in the improvement of their competitiveness. In this paper we describe a prototype system able to discover business processes from an event log and classify them with a suitable level of abstraction with reference to a related business ontology. The identified process, and its corresponding level of abstraction, depends on the knowledge encoded in the reference ontology which is dynamically exploited at runtime. The tool has been validated by considering examples and case studies from the literature on process mining
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g.,
product images or descriptions) as items' side information to improve
recommendation accuracy. While most of such methods rely on factorization
models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be
affected by popularity bias, meaning that it inherently tends to boost the
recommendation of popular (i.e., short-head) items at the detriment of niche
(i.e., long-tail) items from the catalog. Motivated by this assumption, in this
work, we provide one of the first analyses on how multimodality in
recommendation could further amplify popularity bias. Concretely, we evaluate
the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN,
GRCN, LATTICE) on three datasets from Amazon by assessing, along with
recommendation accuracy metrics, performance measures accounting for the
diversity of recommended items and the portion of retrieved niche items. To
better investigate this aspect, we decide to study the separate influence of
each modality (i.e., visual and textual) on popularity bias in different
evaluation dimensions. Results, which demonstrate how the single modality may
augment the negative effect of popularity bias, shed light on the importance to
provide a more rigorous analysis of the performance of such models
URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles
Safety and security issues for Critical Infrastructures are growing as
attackers adopt drones as an attack vector flying in sensitive airspaces, such
as airports, military bases, city centers, and crowded places. Despite the use
of UAVs for logistics, shipping recreation activities, and commercial
applications, their usage poses severe concerns to operators due to the
violations and the invasions of the restricted airspaces. A cost-effective and
real-time framework is needed to detect the presence of drones in such cases.
In this contribution, we propose an efficient radio frequency-based detection
framework called URANUS. We leverage real-time data provided by the Radio
Frequency/Direction Finding system, and radars in order to detect, classify and
identify drones (multi-copter and fixed-wings) invading no-drone zones. We
adopt a Multilayer Perceptron neural network to identify and classify UAVs in
real-time, with % accuracy. For the tracking task, we use a Random Forest
model to predict the position of a drone with an MSE , MAE
, and . Furthermore, coordinate regression is
performed using Universal Transverse Mercator coordinates to ensure high
accuracy. Our analysis shows that URANUS is an ideal framework for identifying,
classifying, and tracking UAVs that most Critical Infrastructure operators can
adopt
- …