Local Popularity and Time in top-N Recommendation

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.Comment: ECIR short paper, 7 page

How to combine visual features with tags to improve movie recommendation accuracy?

Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However, the stylistic visual features can be also used when other sources of information is available (Existing Item scenario). In this work, we address the second scenario and propose a hybrid technique that exploits not only the typical content available for the movies (e.g., tags), but also the stylistic visual content extracted form the movie files and fuse them by applying a fusion method called Canonical Correlation Analysis (CCA). Our experiments on a large catalogue of 13K movies have shown very promising results which indicates a considerable improvement of the recommendation quality by using a proper fusion of the stylistic visual features with other type of features

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

Obesity Indexes and Total Mortality among Elderly Subjects at High Cardiovascular Risk: The PREDIMED Study

BackgroundDifferent indexes of regional adiposity have been proposed for identifying persons at higher risk of death. Studies specifically assessing these indexes in large cohorts are scarce. It would also be interesting to know whether a dietary intervention may counterbalance the adverse effects of adiposity on mortality.MethodsWe assessed the association of four different anthropometric indexes (waist-to-height ratio (WHtR), waist circumference (WC), body mass index (BMI) and height) with all-cause mortality in 7447 participants at high cardiovascular risk from the PREDIMED trial. Forty three percent of them were men (55 to 80 years) and 57% were women (60 to 80 years). All of them were initially free of cardiovascular disease. The recruitment took place in 11 recruiting centers between 2003 and 2009.ResultsAfter adjusting for age, sex, smoking, diabetes, hypertension, intervention group, family history of coronary heart disease, and leisure-time physical activity, WC and WHtR were found to be directly associated with a higher mortality after 4.8 years median follow-up. The multivariable-adjusted HRs for mortality of WHtR (cut-off points: 0.60, 0.65, 0.70) were 1.02 (0.78–1.34), 1.30 (0.97–1.75) and 1.55 (1.06–2.26). When we used WC (cut-off points: 100, 105 and 110 cm), the multivariable adjusted Hazard Ratios (HRs) for mortality were 1.18 (0.88–1.59), 1.02 (0.74–1.41) and 1.57 (1.19–2.08). In all analyses, BMI exhibited weaker associations with mortality than WC or WHtR. The direct association between WHtR and overall mortality was consistent within each of the three intervention arms of the trial.ConclusionsOur study adds further support to a stronger association of abdominal obesity than BMI with total mortality among elderly subjects at high risk of cardiovascular disease. We did not find evidence to support that the PREDIMED intervention was able to counterbalance the harmful effects of increased adiposity on total mortality.Trial RegistrationControlled-Trials.com ISRCTN3573963

Mobile Movie Recommendations with Linked Data

Abstract. The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that computes contextual movie recommendations. Cinemappy refines the recommendation results of a content-based recommender system by exploiting contextual information related to the current spatial and temporal position of the user. The content-based engine leverages graph information within DBpedia, one of the best-known datasets publicly available in the Linked Open Data (LOD) project

Revista del Museo Nacional N° 51

El Museo Nacional, dirigido por Luis E. Valcárcel desde 1931, publicó la Revista del Museo Nacional a partir del año 1932. El presente volumen N° LI, fue publicado en 2016. Contenido: “Identidad, religión y olvido: nuevas religiones en los Andes”; por Marc Ballester i Torrents – “El mojón muyuy en los Andes. Un recorrido por la memoria”; por Beatriz Pérez Galán – “Nuestro pueblo chopcca”; por Pedro Roel Mendizábal; Marleni Martínez Vivanco – “El rito festivo: del qichwariy al llamatumachiy en la microrregión Occollo, Ayacucho”; por Leonor Miluska Muñoz Palomino – “La Fiesta de las Cruces en San Pedro de Casta”; por Patricia Fernández Castillo – “Machuaychas y chiñipilcos: una etnografía de la cachua del 20 de enero en Juliaca”; por Fredy Machicao Castañón – “Danzas del valle del Mantaro”; por Tobías F. Ledesma Mercado – “Insultos y sobrenombres en el sur andino”; por Máximo Cama Ttito – “Tejidos llanos en las colecciones del Museo Nacional de la Cultura Peruana”; por Luis César Ramírez León – “Símbolos de poder, protección y fecundidad en la ornamentación de los muebles de embalaje de cuero coloniales y republicanos”; por Estela Angélica Miranda Castillo – “El Museo Nacional de la Cultura Peruana y la legitimación del arte popular tradicional”; por María Eugenia Yllia Miranda