Recommendation of scientific papers is a task aimed to support researchers in accessing relevant articles from a large pool of unseen articles. When writing a paper, a researcher focuses on the topics related to her/his scientific domain, by using a technical language. The core idea of this paper is to exploit the topics related to the researchers scientific production (authored articles) to formally define her/his profile; in particular we propose to employ topic modeling to formally represent the user profile, and language modeling to formally represent each unseen paper. The recommendation technique we propose relies on the assessment of the closeness of the language used in the researchers papers and the one employed in the unseen papers. The proposed approach exploits a reliable knowledge source for building the user profile, and it alleviates the cold-start problem, typical of collaborative filtering techniques. We also present a preliminary evaluation of our approach on the DBLP.
Just-In-Time Recommender Systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. In this paper, we propose a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for users to initiate any interaction.
Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues.In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.CCS Concepts • Computing methodologies➝Artificial intelligence➝Natural language processing➝Language resources • Computing methodologies➝Machine learning approaches.
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