5,790 research outputs found
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
IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS
Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social
attributes information of destinations is made a factor in the destination recommendation process
Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
Recommender systems are gaining traction in healthcare because they can tailor recommendations
based on users' feedback concerning their appreciation of previous health-related messages. However,
recommender systems are often not grounded in behavioral change theories, which may further increase
the effectiveness of their recommendations. This paper's objective is to describe principles for designing
and developing a health recommender system grounded in the I-Change behavioral change model that
shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon
an existing smoking cessation health recommender system that delivered motivational messages through a
mobile app. A group of experts assessed how the system may be improved to address the behavioral change
determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender
algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages
were designed using 10 health communication methods. The algorithm was designed to match 58 message
characteristics to each user pro le by following the principles of the I-Change model and maintaining the
bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed
to improve the user experience, and this system's design bridges the gap between health recommender
systems and the use of behavioral change theories. This article presents a novel approach integrating
recommender system technology, health behavior technology, and computer-tailored technology. Future
researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112
A large multilingual and multi-domain dataset for recommender systems
This paper presents a multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset
from Twitter messages in English and Italian. The English dataset includes an average of 90 preferences per user on music, books,
movies, celebrities, sport, politics and much more, for about half million users. Preferences are either extracted from messages of
users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their âtopicalâ friends, i.e., followees
representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles
describing them. This unique feature of our dataset provides a mean to derive a semantic categorization of the preferred items, exploiting
available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others
Facilitating Mobile Music Sharing and Social Interaction with Push!Music
Push!Music is a novel mobile music listening and
sharing system, where users automatically receive
songs that have autonomously recommended
themselves from nearby players depending on similar
listening behaviour and music history. Push!Music
also enables users to wirelessly send songs between
each other as personal recommendations. We
conducted a two-week preliminary user study of
Push!Music, where a group of five friends used the
application in their everyday life. We learned for
example that the shared music in Push!Music became
a start for social interaction and that received songs in
general were highly appreciated and could be looked
upon as âtreatsâ
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A personâs
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the userâs experience than generic
parameters, accurate predictions reveal important aspects of userâs
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumerâs buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five usersâ
personality factors and 1,200 personal usersâ pictures
Improving the Dependability of Destination Recommendations using Information on Social Aspects
Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation
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