1,431 research outputs found
Whatâs going on in my city? Recommender systems and electronic participatory budgeting
In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities â Cambridge, Miami and New York Cityâ, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area
Language in Our Time: An Empirical Analysis of Hashtags
Hashtags in online social networks have gained tremendous popularity during
the past five years. The resulting large quantity of data has provided a new
lens into modern society. Previously, researchers mainly rely on data collected
from Twitter to study either a certain type of hashtags or a certain property
of hashtags. In this paper, we perform the first large-scale empirical analysis
of hashtags shared on Instagram, the major platform for hashtag-sharing. We
study hashtags from three different dimensions including the temporal-spatial
dimension, the semantic dimension, and the social dimension. Extensive
experiments performed on three large-scale datasets with more than 7 million
hashtags in total provide a series of interesting observations. First, we show
that the temporal patterns of hashtags can be categorized into four different
clusters, and people tend to share fewer hashtags at certain places and more
hashtags at others. Second, we observe that a non-negligible proportion of
hashtags exhibit large semantic displacement. We demonstrate hashtags that are
more uniformly shared among users, as quantified by the proposed hashtag
entropy, are less prone to semantic displacement. In the end, we propose a
bipartite graph embedding model to summarize users' hashtag profiles, and rely
on these profiles to perform friendship prediction. Evaluation results show
that our approach achieves an effective prediction with AUC (area under the ROC
curve) above 0.8 which demonstrates the strong social signals possessed in
hashtags.Comment: WWW 201
Streamlining Knowledge Graph Construction with a fa\c{c}ade: The SPARQL Anything project
What should a data integration framework for knowledge engineers look like?
Recent research on Knowledge Graph construction proposes the design of a
fa\c{c}ade, a notion borrowed from object-oriented software engineering. This
idea is applied to SPARQL Anything, a system that allows querying heterogeneous
resources as-if they were in RDF, in plain SPARQL 1.1, by overloading the
SERVICE clause. SPARQL Anything supports a wide variety of file formats, from
popular ones (CSV, JSON, XML, Spreadsheets) to others that are not supported by
alternative solutions (Markdown, YAML, DOCx, Bibtex). Features include querying
Web APIs with high flexibility, parametrised queries, and chaining multiple
transformations into complex pipelines. In this paper, we describe the design
rationale and software architecture of the SPARQL Anything system. We provide
references to an extensive set of reusable, real-world scenarios from various
application domains. We report on the value-to-users of the founding
assumptions of its design, compared to alternative solutions through a
community survey and a field report from the industry.Comment: 15 page
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