4,510 research outputs found
Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations
This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.Paula Viana and Márcio Soares were partial supported by Project “TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”, under Research Line FourEyes, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Paula Viana has also been supported by National Funds through the Portuguese funding agency, FCT—Fundação para a CiĂŞncia e a Tecnologia, within project UIDB/50014/2020. Rita Gaio was partially supported by CMUP, which is Financed by national funds through FCT—Fundação para a CiĂŞncia e a Tecnologia, I.P., under the project with the reference UIDB/00144/2020. AmĂlcar Correia was partially supported by the Project Pglobal (Nr. 2014/38592-Programa Operacional Temático Factores de Competitividade/Programa Operacional do Norte, Funded by ERDF).info:eu-repo/semantics/publishedVersio
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
THOR: A Hybrid Recommender System for the Personalized Travel Experience
One of the travelers’ main challenges is that they have to spend a great effort to find and
choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized
items. Recommendation systems provide an effective way to solve the problem of information
overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid,
personalized recommender system for the transportation domain. THOR assigns every traveler a
unique contextual preference model built using solely their personal data, which makes the model
sensitive to the user’s choices. This model is used to rank travel offers presented to each user
according to their personal preferences. We reduce the recommendation problem to one of binary
classification that predicts the probability with which the traveler will buy each available travel
offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal
preference model. Moreover, to tackle the cold start problem for new users, we apply clustering
algorithms to identify groups of travelers with similar profiles and build a preference model for each
group. To test the system’s performance, we generate a dataset according to some carefully designed
rules. The results of the experiments show that the THOR tool is capable of learning the contextual
preferences of each traveler and ranks offers starting from those that have the higher probability of
being selected
Context-Aware Recommendation Systems in Mobile Environments
Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /
FamTV : an architecture for presence-aware personalized television
Since the advent of the digital era, the traditional TV scenario has rapidly evolved towards an ecosystem comprised of a myriad of services, applications, channels, and contents. As a direct consequence, the amount of available information and configuration options targeted at today's end consumers have become unmanageable. Thus, personalization and usability emerge as indispensable elements to improve our content-overloaded digital homes. With these requirements in mind, we present a way to combine content adaptation paradigms together with presence detection in order to allow a seamless and personalized entertainment experience when watching TV.This work has been partially supported by the Community of Madrid (CAM), Spain under the contract number S2009/TIC-1650.Publicad
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
- …