Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation

In movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users’ preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user’s preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.info:eu-repo/semantics/publishedVersio

Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.info:eu-repo/semantics/publishedVersio

RD-Connect, NeurOmics and EURenOmics: collaborative European initiative for rare diseases

Functional Genomics of Muscle, Nerve and Brain Disorder

Vascular and ductal elastotic changes in pancreatic cancer

This study aims to identify and define the type and frequency of elastotic alterations of vessels and ducts in pancreatic ductal carcinoma (PDAC) and evaluate its diagnostic significance. Representative tissue from 36 Whipple specimens, stained with Verhoeff's Van-Gieson, was studied focusing on the density and distribution of elastic fibers in walls of vessels and ducts, in perivascular and periductal tissue and in tumor stroma. Vessels and ducts within the carcinoma, at tumor periphery and in non-tumoral pancreas were grouped and examined separately. Vimentin and α-SMA immunostains were used for the depiction of fibroblasts and myofibroblasts. Histochemistry revealed mild to severe elastotic changes of vessels and ducts in all examined cases. Vascular and ductal elastosis was more prominent within the tumor and diminished at tumor periphery. In tumor stroma and non-tumoral pancreatic tissue mild or no elastosis was identified. α-SMA+ cells were observed in large numbers in tumor stroma and as a ring around carcinomatous structures. There were scant α-SMA+ cells around elastotic and non-elastotic vessels. Conclusively, vascular and ductal elastosis is a tumor-associated phenomenon in PDAC. Its presence is indicative of benignity acquiring a possible diagnostic role. © 2016 APMIS Published by John Wiley & Sons Ltd

A Personalized Location Aware Multi-Criteria Recommender System Based on Context-Aware User Preference Models

Part 2: Data MiningInternational audienceRecommender Systems have been applied in a large number of domains. However, current approaches rarely consider multiple criteria or the level of mobility and location of a user. In this paper, we introduce a novel algorithm to construct personalized multi-criteria Recommender Systems. Our algorithm incorporates the user’s current context, and techniques from the Multiple Criteria Decision Analysis field of study to model user preferences. The obtained preference model is used to assess the utility of each item, to then recommend the items with the highest utility. The criteria considered when creating preference models are the user location, mobility level and user profile. The latter is obtained considering the user requirements, and generalizing the user data from a large-scale demographic database. The evaluation of our algorithm shows that our system accurately identifies the demographic groups where a user may belong, and generates highly accurate recommendations that match his/her preference value scale

Cardiac involvement and subsequent death due to extranodal nk/t cell cutaneous t-cell lymphoma: An autopsy case and brief review of the literature

Cardiac tumors range from benign to high grade malignancies. The incidence of cardiac involvement either by primary, or secondary tumors during autopsy is reported to be extremely low. Extranodal NK/T-cell lymphoma (ENKTL), nasal type is an unusual type of lymphoma. The skin is the second most common site of involvement after the respiratory tract. We present a case of a 63-year-old male, who was recently diagnosed with ENKTL, nasal type, who received chemotherapy, and died without any evident cause. The corpse was referred for routine medicolegal examination. Macroscopical determination of the cause of death was not feasible and subsequent histopathological examination revealed heart infiltration by ENKTL that was found in vivo in cutaneous lesions. Similar infiltrations existed in the pancreatic tissue. To the best of our knowledge, myocardial infiltration of ENKTL, inducing severe myocardial lesions that eventually caused death, is rare, with limited cases reported in the literature. © 2021