1,721,010 research outputs found
Adaptive Multi-attribute Diversity for Recommender Systems
Full text linkProviding very accurate recommendations to end users has been nowadays recognized to be just one of the tasks an effective recommender system should accomplish. While predicting relevant suggestions, attention needs to be paid also to their diversification in order to avoid monotony in the returned list of recommendations. In this paper we focus on modeling user propensity toward selecting diverse items, where diversity is computed by means of content-based item attributes. We then exploit such modeling to present a novel approach to re-arrange the list of Top-N items predicted by a recommendation algorithm, with the aim of fostering diversity in the final ranking. An extensive experimental evaluation proves the effectiveness of the proposed approach as well as its ability to improve also novelty and catalog coverage values
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Functional outcomes of copy number variations of Chrna7 gene
No full textThe discovery of genomic rearrangements, known as copy number variations
(CNVs), is relatively new; their contribution to genetic heterogeneity and their
impact on human diseases is still largely unknown. CNVs within the human
15q13.3 region have been associated with neuropsychiatric and neurodevelopmental
disorders such as schizophrenia, autism spectrum disorders (ASD),
intellectual disability, and epilepsy. Several recent studies suggest that, within this
chromosomic region, the gene CHRNA7, encoding for the human alfa7 subunit of
the nicotinic acetylcholine receptor, plays a major role in the observed pathological
phenotypes. Consistently, patients carrying deletions or duplications of CHRNA7
present symptoms comparable to patients with a larger 1.5 Mb deletion of the
15q13.3 region. The penetrance of CHRNA7 CNVs is variable, and the exact
pathogenic mechanisms are currently being elucidated.
In this chapter, we have critically reviewed all up-to-date studies regarding the
functional outcomes of CNVs involving the alpha7-nicontinic receptor
(a7nAChR), highlighting the advantages and disadvantages of the methodologies
and models utilized. We have described the structure, functionality, and physiological
role of the a7nAChR, analyzing the mechanisms that determine the
occurrence of CNVs and the clinical features of patients carrying CNRNA7 CNVs.
We have examined and compared the mice models used to study the role of these
CNVs and the new human model of induced pluripotent stem cells, which is proving
very useful in clarifying the clinical and phenotypic features pertaining specifically
to humans
Influence of the Prosumer Allocation and Heat Production on a District Heating Network
Full text linkTo face the climate change and global warming issues, European countries have set new targets in order to reduce the CO2 emissions to 40% by 2030 and to 80% by 2050. The district heating networks, and in particular low-temperature networks, due to their efficient heat supply and distribution represent a key point for meeting these goals, as well as the renewable sources integration. Nowadays, in fact, about 40% of the energy consumed in Europe is for heating, most of which is provided by fossil fuels employment. This article concerns the smart district heating, namely, the possibility of a bidirectional energy exchange between the district heating network and the connected users. The main purpose of this study is to evaluate the possibility of including a prosumer-that is, a customer who can both consume and produce heat-in an existing small/medium district heating network. To this purpose, an in-house-developed software has been applied to analyze whether and which user of the district heating network is more suitable to be set as prosumer and the effect of the installed distributed generation system on the network. The results show how the choice of a prosumer over another and how the amount of exchanged thermal power affect the performance of the network, with a consequent need of a modification in its operation and management
RDF graph embeddings for content-based recommender systems
No full textLinked Open Data has been recognized as a useful source of background knowledge for building content-based recommender systems. Vast amount of RDF data, covering multiple domains, has been published in freely accessible datasets. In this paper, we present an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs used for building content-based recommender system. We generate sequences by leveraging local information from graph sub-structures and learn latent numerical representations of entities in RDF graphs. Our evaluation on two datasets in the domain of movies and books shows that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be effectively used in content-based recommender systems
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