Semantic disambiguation and contextualisation of social tags

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28509-7_18This manuscript is an extended version of the paper ‘cTag: Semantic Contextualisation of Social Tags’, presented at the 6th International Workshop on Semantic Adaptive Social Web (SASWeb 2011).We present an algorithmic framework to accurately and efficiently identify the semantic meanings and contexts of social tags within a particular folksonomy. The framework is used for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies. The analysis includes a comparison between semantic similarities obtained for pairs of tags in Delicious folksonomy, and their semantic distances in the whole Web, according to co-occurrence based metrics computed with results of a Web search engine.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877

Aplicación del análisis dedrocronológico de Retama sphaerocarpa L. (Boiss) para datar el abandono agrícola

Abandonment of agricultural land leads to changes in soil characteristics that may result in better or worse soil conditions. These changes are slow therefore the use of indicators for dating the time of abandonment is particularly useful. This study was carried out in Madrid, Spain with the aim to establish for the first time the use of Retama sphaerocarpa L. (Boiss) as a dendrochronological tool for dating land abandonment. This offers the possibility to take into consideration a period of time long enough for changes in soil to be determined. Such changes can be indicated by fluctuations in soil organic carbon content (SOC), porosity or water availability. Three different situations resulted from the dendrochronological analysis: soil currently tilled; soil recently abandoned (less than 5 years), and prolonged abandonment (in average 10 years). In addition the influence of Retama sphaerocarpa L. (Boiss) on soils was checked for these periods of abandonment. The rate of SOC gain can be considered fast. Tilled soils accounted for 0.48% SOC, and reached 1% in less than 5 years, although with wide standard deviations. Due to prolonged abandonment SOC reached 1.41%, (P = 0.09). Total soil porosity under tillage was 49%, and decreased to 38% after 4-5 years, but recovered to 41% under prolonged abandonment. Water availability (volumetric soil moisture between field capacity and permanent wilting point) remained the same, ranging from 7.7 to 8.5% along the whole period of time. The presence of R. sphaerocarpa L. (Boiss) accelerates soil changes as SOC in prolonged abandonment increased to 2.65%, porosity was 41% and water availability 10.3

Modeling tourists' personality in recommender systems: how does personality influence preferences for tourist attractions?

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality.GrouPlanner Project under the European Regional Development Fund POCI-01-0145-FEDER29178 and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UIDB/00319/2020 and UIDB/00760/202

Role and task recommendation and social tagging to enable social business process management

Traditional Business Process Management (BPM) poses a number of limitations for the management of ad-hoc processes, where the execution paths are not designed a priori and evolve during enactment. Social BPM, which predicates to integrate social software into the BPM lifecycle, has emerged as an answer to such limitations. This paper presents a framework for social BPM in which social tagging is used to capture process knowledge emerging during the enactment and design of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. Such knowledge is exploited by recommendation tools to support the design and enactment of future process instances. We first provide an overview of our framework, introducing the concepts of role and task recommendations, which are supported by social tagging. These mechanisms are then elaborated further by an example. Eventually, we discuss a prototype of our framework enabling collaborative process design and execution

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

Semantic contextualisation of social tag-based profiles and item recommendations

Proceedigns of 12th International Conference, EC-Web 2011, Toulouse, France, August 30 - September 1, 2011.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23014-1_9We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and the Community of Madrid (CCG10- UAM/TIC-5877

Extending sound sample descriptions through the extraction of community knowledge

Comunicació presentada a la 19th International Conference (UMAP) que va tenir lloc de l'11 al 15 de juliol a Girona.Sound and music online services driven by communities of users are filled with large amounts of user-created content that has to be properly described. In these services, typical sound and music modeling is performed using either content-based or context-based strategies, but no special emphasis is given to the extraction of knowledge from the community. We outline a research plan in the context of Freesound.org and propose ideas about how audio clip sharing sites could adapt and take advantage of particular user communities to improve the descriptions of their content.This work is partially supported under BES-2010-037309 FPI grant from the Spanish Ministry of Science and Innovation for the TIN2009- 14247-C02-01 DRIMS project