167 research outputs found
Special Issue on: Personalisation in E-Government and Smart Cities
The abstract is included in the text
What is the role of context in fair group recommendations?
We investigate the role played by the context, i.e. the situation the group is currently experiencing, in the design of a system that recommends sequences of activities as a multi-objective optimization problem, where the satisfaction of the group and the available time interval are two of the functions to be optimized. In particular, we highlight that the dynamic evolution of the group can be the key contextual feature that has to be considered to produce fair suggestions
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readersâ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
Using Gaze for Behavioural Biometrics
A principled approach to the analysis of eye movements for behavioural biometrics is laid
down. The approach grounds in foraging theory, which provides a sound basis to capture the unique-
ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for
quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel-
evant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are
shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished
via a classical classification technique. A proof of concept of the method is provided by measuring
its identification performance on a publicly available dataset. Data and code for reproducing the
analyses are made available. Overall, we argue that the approach offers a fresh view on either the
analyses of eye-tracking data and prospective applications in this field
Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation
Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity.
Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity.
Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions.
State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers.
To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art.
Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering.
In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari
A clustering approach for the analysis of InSAR Time Series: application to the Bandung Basin (Indonesia)
Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract meaningful information. We propose a novel framework for interpreting the large number of ground displacement measurements derived from InSAR time series techniques using a three-step process: (1) dimensionality reduction of the displacement time series from an InSAR data stack; (2) clustering of the reduced dataset; and (3) detecting and quantifying accelerations and decelerations of deforming areas using a change detection method. The displacement rates, spatial variation, and the spatio-temporal nature of displacement accelerations and decelerations are used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to potential causal and triggering factors. We tested the method over the Bandung Basin in Indonesia using Sentinel-1 data processed with the small baseline subset InSAR time series technique. The results showed widespread subsidence in the central basin with rates up to 18.7 cm/yr. We identified 12 main clusters of subsidence, of which three covering a total area of 22 km2 show accelerating subsidence, four clusters over 52 km2 show a linear trend, and five show decelerating subsidence over an area of 22 km2. This approach provides an objective way to monitor and interpret ground movements, and is a valuable tool for understanding the physical behaviour of large deforming areas
USER CONTROLLABILITY IN A HYBRID RECOMMENDER SYSTEM
Since the introduction of Tapestry in 1990, research on recommender systems has traditionally focused on the development of algorithms whose goal is to increase the accuracy of predicting usersâ taste based on historical data. In the last decade, this research has diversified, with human factors being one area that has received increased attention. Usersâ characteristics, such as trusting propensity and interest in a domain, or systemsâ characteristics, such as explainability and transparency, have been shown to have an effect on improving the user experience with a recommender. This dissertation investigates on the role of controllability and user characteristics upon the engagement and experience of users of a hybrid recommender system. A hybrid recommender is a system that integrates the results of different algorithms to produce a single set of recommendations. This research examines whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) results in increased engagement and a better user experience. The essential contribution of this dissertation is an extensive study of controllability in a hybrid fusion scenario. In particular, the introduction of an interactive Venn diagram visualization, combined with sliders explored in a previous work, can provide an efficient visual paradigm for information filtering with a hybrid recommender that fuses different prospects of relevance with overlapping recommended items. This dissertation also provides a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures
Preference Learning
This report documents the program and the outcomes of Dagstuhl Seminar 14101 âPreference Learningâ. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies
How Fast Can We Play Tetris Greedily With Rectangular Pieces?
Consider a variant of Tetris played on a board of width and infinite
height, where the pieces are axis-aligned rectangles of arbitrary integer
dimensions, the pieces can only be moved before letting them drop, and a row
does not disappear once it is full. Suppose we want to follow a greedy
strategy: let each rectangle fall where it will end up the lowest given the
current state of the board. To do so, we want a data structure which can always
suggest a greedy move. In other words, we want a data structure which maintains
a set of rectangles, supports queries which return where to drop the
rectangle, and updates which insert a rectangle dropped at a certain position
and return the height of the highest point in the updated set of rectangles. We
show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on
a board of width , if the OMv conjecture [Henzinger et al., 2015]
is true, then both operations cannot be supported in time
simultaneously. The reduction also implies polynomial bounds from the 3-SUM
conjecture and the APSP conjecture. On the other hand, we show that there is a
data structure supporting both operations in time on
boards of width , matching the lower bound up to a factor.Comment: Correction of typos and other minor correction
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