SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships

In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles

Has Xylella fastidiosa "chosen" olive trees to establish in the Mediterranean basin?

<p>Severe decline of olive trees was observed in the Lecce province, Apulia (Italy), and received the name ‘complesso del disseccamento rapido dell’olivo’ (olive rapid decline complex). Affected plants showed leaf scorch symptoms and dieback of twigs, branches and even of the whole plant. Similar symptoms, unusual for the area, have also been observed in other Apulian localities (Cerignola, Foggia, Canosa di Puglia, and Andria). Three fungal species were associated with the symptoms: Phaeoacremonium aleophilum, Neofusicoccum parvum, and Pleurostomophora richardsiae. The latter is the first report of this fungal species infecting olives. In the Lecce province, the bacterium Xylella fastidiosa also was detected from affected olive trees. Xylella fastidiosa is a quarantine agent in Europe that had been previously reported in the Mediterranean region, but did not spread probably because of the lack of a vector. Present findings suggest that this fundamental condition has now been met.</p

A domain-independent framework for building conversational recommender systems

Conversational Recommender Systems (CoRSs) implement a paradigm where users can interact with the system for defining their preferences and discovering items that best fit their needs. A CoRS can be straightforwardly implemented as a chatbot. Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. In the most complex form, the implementation of a chatbot is a challenging task since it requires knowledge about natural language processing, human-computer interaction, and so on. In this paper, we propose a general framework for making easy the generation of conversational recommender systems. The framework, based on a content-based recommendation algorithm, is independent from the domain. Indeed, it allows to build a conversational recommender system with different interaction modes (natural language, buttons, hybrid) for any domain. The framework has been evaluated on two state-of-the-art datasets with the aim of identifying the components that mainly influence the final recommendation accuracy

HPLC-HRMS Global Metabolomics Approach for the Diagnosis of “Olive Quick Decline Syndrome” Markers in Olive Trees Leaves

Olive quick decline syndrome (OQDS) is a multifactorial disease affecting olive plants. The onset of this economically devastating disease has been associated with a Gram-negative plant pathogen called Xylella fastidiosa (Xf). Liquid chromatography separation coupled to high-resolution mass spectrometry detection is one the most widely applied technologies in metabolomics, as it provides a blend of rapid, sensitive, and selective qualitative and quantitative analyses with the ability to identify metabolites. The purpose of this work is the development of a global metabolomics mass spectrometry assay able to identify OQDS molecular markers that could discriminate between healthy (HP) and infected (OP) olive tree leaves. Results obtained via multivariate analysis through an HPLC-ESI HRMS platform (LTQ-Orbitrap from Thermo Scientific) show a clear separation between HP and OP samples. Among the differentially expressed metabolites, 18 different organic compounds highly expressed in the OP group were annotated; results obtained by this metabolomic approach could be used as a fast and reliable method for the biochemical characterization of OQDS and to develop targeted MS approaches for OQDS detection by foliage analysis

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

The choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy