Search

Many governments and firms do believe that technology can supplant governance and human responsibility. This belief poses the question of who will really benefit from smart cities. This article explores this fundamental question through the study of digital media platforms. The ultimate goal is to understand the link between e-governance and smart city initiatives in our cases of study by testing whether these projects are explicitly for citizens. This article shows how e-platforms represent the use of information and communication technologies with the aim of encouraging citizen participation in decision-making processes, improving information and service delivery, reinforcing transparency, accountability, as well as credibility. Thirteen digital media platforms are surveyed, mostly in cities across countries. These e-platforms raise implementation challenges for both firms and policy makers, and new research opportunities for scientist to build up new research and to experiment with the aim to make the benefits for citizens wider and the participatory dimension stronger. more...

We are witnessing an increasing interest in building recommender systems (RSs) for all sorts of Software Engineering activities. Modelling is no exception to this trend, as modelling environments are being enriched with RSs that help building models by providing recommendations based on previous solutions to similar problems in the same domain. However, building a RS from scratch requires considerable effort and specialized knowledge. To alleviate this problem, we propose an automated approach to the generation of RSs for modelling languages. Our approach is model-based, and we provide a domain-specific language called Droid to configure every aspect of the RS (like the type and features of the recommended items, the recommendation method, and the evaluation metrics). The RS so configured can be deployed as a service, and we offer out-of-the-box integration of this service with the EMF tree editor. To assess the usefulness of our proposal, we present a case study on the integration of a generated RS with a modelling chatbot, and report on an offline experiment measuring the precision and completeness of the recommendations, This project has received funding from the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813884, the Spanish Ministry of Science (RTI2018-095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314) more...

With the advent of smart cities (SCs), governance has been placed at the core of the debate on how to create public value and achieve a high quality of life in urban environments. In particular, given that public value is rooted in democratic theory and new technologies that promote networking spaces have emerged, citizen participation represents one of the principal instruments to make government open and close to the citizenry needs. Participation in urban governance has undergone a great development: from the first postmodernist ideals of countering expert dominance to today’s focus on learning and social innovation, where citizen participation is conceptualized as co-creation and co-production. Despite this development, there is a lack of research to know how this new governance context is taking place in the SC arena. Addressing this situation, in this article, we present an exhaustive survey of the research literature and a deep study of the experience in participative initiatives followed by SCs in Europe. Through an analysis of 149 SC initiatives from 76 European cities, we provide interesting insights about how participatory models have been introduced in the different areas and dimensions of the cities, how citizen engagement is promoted in SC initiatives, and whether the so-called creative SCs are those with a higher number of projects governed in a participatory way, This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P), and the Spanish Ministry of Culture and Sport (CAS18/00035) more...

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and modelbased development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research more...

Smart Cities are conceived as strategic models to confront the wicked problems that exist in urban contexts. The research literature, however, reflects a lack of consensus on the elements that make a city “smart.” While some authors focus on technological aspects, others consider human factors as principal targets of the cities’ initiatives. Aiming to shed light on this discrepancy and understand what makes a city smarter, in this paper, we analyze a large number of real case studies implemented in major European Smart Cities. From our analysis, we first characterize and categorize the cities according to theoretical Smart City models proposed in the literature. Based on the cities’ characteristics and categories, we then compare them according to external variables, such as their positions in worldwide Smart City rankings, and their administrative contexts, This work was supported by the Spanish Ministries of Economy, Industry and Competitiveness (TIN2016-80630-P) and Science, Innovation and Universities (CAS18/00035) more...

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, With the continuous growth of scientific literature, discovering relevant academic papers for a researcher has become a challenging task, especially when looking for the latest, most recent papers. In this case, traditional collaborative filtering systems are ineffective, since they are unable to recommend items not previously seen, rated or cited. This is known as the item cold-start problem. In this paper, we explore the potential of exploiting citation knowledge to provide a given user with relevant suggestions about recent scientific publications. A novel hybrid recommendation method that encapsulates such citation knowledge is proposed. Experimental results show improvements over baseline methods, evidencing benefits of using citation knowledge to recommend recently published papers in a personalised way. Moreover, as a result of our work, we also provide a unique dataset that, differently to previous corpora, contains detailed paper citation information more...

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexicons, This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P). more...

Many governments and firms do believe that technology can supplant governance and human responsibility. This belief poses the question of who will really benefit from smart cities. This article explores this fundamental question through the study of digital media platforms. The ultimate goal is to understand the link between e-governance and smart city initiatives in our cases of study by testing whether these projects are explicitly for citizens. This article shows how e-platforms represent the use of information and communication technologies with the aim of encouraging citizen participation in decision-making processes, improving information and service delivery, reinforcing transparency, accountability, as well as credibility. Thirteen digital media platforms are surveyed, mostly in cities across countries. These e-platforms raise implementation challenges for both firms and policy makers, and new research opportunities for scientist to build up new research and to experiment with the aim to make the benefits for citizens wider and the participatory dimension stronger This work has been supported by Grant CITADEL H2020 Empowering Citizens to Transform European PubLic Administrations. H2020 Framework Program (under GA 726755, running from 1/9/2016 to 1/9/2020) directed by Daniel Diaz-Fuentes, University of Cantabria --This is a pan European project with the innovation Basque firm TECNALIA as general coordinator. This work has has also been supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P) more...

This manifesto summarises the outcomes of the 1st Workshop on Recommender Systems for Citizens (CitRec'17), held at the 11th ACM Conference on Recommender Systems, in August 2017 in Como, Italy. We discuss challenges and opportunities for the development of recommender systems for citizens, including: the clarification of the role of recommender systems for cities and citizens; in this context, the identification of classes of items to be recommended; the need for targeting and engaging the right population, involving the right stakeholders; and the existence of underlying ethical issues such as fairness and consensus. We further provide an action plan to bring forward the research and application of recommender systems for citizens. more...

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes, This work was partially supported by the national Spanish Government (grants nr. TIN2013-47090-C3-2 and TIN2016-80630-P). We wish to express our gratitude to the anonymous reviewers whose insightful and generous feedback guided us in producing an enhanced version of the paper beyond the amendmentof flaws and shortcomings more...

Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users — often by means of ratings —, or implicitly acquired by a system — for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. the thumbs up/down in YouTube, and the likes in Facebook, are increasingly popular, and make the user cold start problem even more challenging. To address the cold start with positive-only feedback situations, we propose to exploit data additional to user preferences by means of specialized hybrid recommendation methods. In particular, we investigate a number of graph-based and matrix factorization recommendation models that jointly exploit user preferences and item semantic metadata automatically extracted from the well-known knowledge graph of DBpedia. Following a rigorous evaluation methodology for cold start, we empirically compare the above hybrid recommendation models on a Facebook dataset containing users likes for items in three different domains, namely books, movies and music. The achieved experimental results show that the semantics-aware hybrid approaches we propose outperform content-based and collaborative filtering baselines. In addition to recommendation accuracy, in our evaluation we also consider individual and aggregate diversity of recommendations as key quality factors in the users’ satisfaction. more...

In recommender systems, user preferences can be acquired either explicitly by means of ratings, or implicitly --e.g., by processing text reviews, and by mining item browsing and purchasing records. Most existing collaborative filtering approaches have been designed to deal with numerical ratings, such as the 5-star ratings in Amazon and Netflix, for both rating prediction and item ranking (a.k.a. top-N recommendation) tasks. In many e-commerce and social network sites, however, user preferences are usually expressed in the form of binary and unary (positive-only) ratings, such as the thumbs up/down in YouTube and the likes in Facebook, respectively. Moreover, in these cases, the well-known problem of cold-start --i.e., the scarcity of user preferences-- is highly remarkable. To address this situation, we explore a number of graph-based and matrix factorization recommendation models that jointly exploit user ratings and item metadata. In this work, such metadata are automatically obtained from DBpedia --the queriable and structured version of Wikipedia which is considered as the core knowledge repository of the Linked Open Data initiative--, and the models are evaluated with a Facebook dataset covering three distinct domains, namely books, movies and music. The results achieved in our experiments show that the proposed hybrid recommendation models, which exploit rating and semantic data, outperform content-based and collaborative filtering baselines. more...

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13, Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013., Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain., This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542) more...

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_5, Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013., In this paper we present a contextual modeling approach for model-based recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations. more...

Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources. more...

Folksonomies emerge as the result of the free tagging activity of a large number of users over a variety of resources. They can be considered as valuable sources from which it is possible to obtain emerging vocabularies that can be leveraged in knowledge extraction tasks. However, when it comes to understanding the meaning of tags in folksonomies, several problems mainly related to the appearance of synonymous and ambiguous tags arise, specifically in the context of multilinguality. The authors aim to turn folksonomies into knowledge structures where tag meanings are identified, and relations between them are asserted. For such purpose, they use DBpedia as a general knowledge base from which they leverage its multilingual capabilities. more...

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management, http://dx.doi.org/10.1145/2396761.2398760., This paper provides an overview of the 1st International Workshop on Multimodal Crowd Sensing (CrowdSens 2012), held at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012). This workshop aimed to provide an open forum for researchers from various fields such as fields such as Natural Language Processing, Information Extraction, Data Mining, Information Retrieval, User Modeling and Personalization, Stream Processing, and Sensor Networks, for addressing the challenges of effectively mining, analyzing, fusing, and exploiting information sourced from multimodal physical and social sensor data sources., We thank the EU-FP7 project WeGov (grant 248512), and the Spanish project i3media (CENIT-2007-1012) for sponsoring the workshop. more...

In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks. Current folksonomy-based search and recommendation models exploit the social tag space as a whole to retrieve those items relevant to a tag-based query or user profile, and do not take into consideration the purposes of tags. We hypothesise that a significant percentage of tags are noisy for content retrieval, and believe that the distinction of the personal intentions underlying the tags may be beneficial to improve the accuracy of search and recommendation processes. We present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Our approach finds the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. The obtained concepts are then transformed into semantic classes that can be uniquely assigned to content- and context-based categories. The identification of subjective and organisational tags is based on natural language processing heuristics. We collected a representative dataset from Flickr social tagging system, and conducted an empirical study to categorise real tagging data, and evaluate whether the resultant tags categories really benefit a recommendation model using the Random Walk with Restarts method. The results show that content- and context-based tags are considered superior to subjective and organisational tags, achieving equivalent performance to using the whole tag space. more...