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La raccomandazione di articoli scientifici è un compito che mira a migliorare lo sfruttamento delle biblioteche digitali (DL) e aiuta i ricercatori a trovare articoli pertinenti da un ampio pool di articoli. Tuttavia, devono essere fornite fonti affidabili per modellare gli interessi dei ricercatori in modo da avere raccomandazioni precise. In questa tesi, ci concentriamo sull’estrazione degli interessi attuali degli utenti dai articoli a cui l’utente è collegato (autore o valutato) e anche utilizzando la struttura sociale della rete accademica dell’utente (relazioni tra ricercatori nello stesso dominio). Abbiamo proposto un approccio di filtraggio basato sul contenuto per la raccomandazione di articoli scientifici che si basa sulla modellazione di temi: il profilo di un ricercatore è modellato da una serie di temi ottenuti applicando Latent Dirichlet Allocation (LDA) alli articoli scientifici dal ricercatore. Il profilo creato da questo modello è facilmente interpretabile e può spiegare i risultati della raccomandazione. Gli utenti nei sistemi di raccomandazione di articoli scientifici hanno poche valutazioni che non consentono agli approcci di filtraggio basato sul contenuto di discriminare gli articoli che l’utente ama da altri a cui non gli piacciono. A tale scopo, abbiamo proposto un modello di raccomandazione che misura la differenza tra due distribuzioni di probabilità tra il modello tematico del corpus del ricercatore e i modelli linguistici di nuovi documenti. Nei sistemi di raccomandazione di articoli scientifici, la matrice di valutazione è molto sparse e gli utenti sono relativamente pochi rispetto ai numerosi articoli disponibili. A superare il problema della scarsità di dati negli approcci di filtraggio collaborativo, noi ha proposto un approccio di raccomandazione di articoli scientifici che combina l’analisi del contenuto basato su tecniche LDA, reti sociali per la generazione di vicinati, e il modello linguistico basato sulla pertinenza alla raccoma, Scientific paper recommendation is a task that aims to enhance the exploitation of Digital Libraries (DL) and helps researchers to find relevant papers from a large pool of papers. However, reliable sources to model the researcher interests must be provided to have accurate recommendations. In this thesis, we focus on the extraction of the user topical interests from papers that the user is connected with (authored or rated) and also by using the social structure of the academic network of the user (relations among researchers in the same domain). We proposed a fully Content-Based Filtering (CBF) approach for scientific paper recommendation that relies on topic modeling: the profile of a researcher is modeled by a set of topics obtained by applying Latent Dirichlet Allocation (LDA) to the papers written by the researcher. The profile built by this model is easily interpretable, and can explain the recommendation results. Users in recommender systems of scientific papers have few ratings that do not allow CBF approaches to discriminate papers the user likes from others she/he doesn’t like. For this purpose, we proposed a recommendation model which measures the difference between two probability distributions between the topic model of the researcher’s corpus and the language models of new papers and rank papers by minimizing the difference. In recommender systems of scientific papers, the user-item rating matrix is very sparse and users are relatively few compared with the numerous available items. To overcome the issue of data sparsity in Collaborative Filtering (CF) approaches, we proposed a scientific paper recommendation approach which combines content analysis based on LDA, Social Networks (SN) techniques for neighborhood generation, and the relevance based language model to CF recommendation. This approach uses the topics in the researcher’s rated papers to define the user profiles, thus ignoring the numeric values of ratings, and applying a community detection

When looking for recently published scientific papers, a researcher usually focuses on the topics related to her/his scientific interests. The task of a recommender system is to provide a list of unseen papers that match these topics. The core idea of this paper is to leverage the latent topics of interest in the publications of the researchers, and to take advantage of the social structure of the researchers (relations among researchers in the same field) as reliable sources of knowledge to improve the recommendation effectiveness. In particular, we introduce a hybrid approach to the task of scientific papers recommendation, which combines content analysis based on probabilistic topic modeling and ideas from collaborative filtering based on a relevance-based language model. We conducted an experimental study on DBLP, which demonstrates that our approach is promising.

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.

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.