A Distributed and Accountable Approach to Offline Recommender Systems Evaluation

Different software tools have been developed with the purpose of performing offline evaluations of recommender systems. However, the results obtained with these tools may be not directly comparable because of subtle differences in the experimental protocols and metrics. Furthermore, it is difficult to analyze in the same experimental conditions several algorithms without disclosing their implementation details. For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion. By relying on consolidated web protocols, we created RESTful APIs for training and querying recommenders remotely. In this way, it is possible to easily integrate into the same toolkit algorithms realized with different technologies. In details, the experimenter can perform an evaluation by simply visiting a web interface provided by RecLab. The framework will then interact with all the selected recommenders and it will compute and display a comprehensive set of measures, each representing a different metric. The results of all experiments are permanently stored and publicly available in order to support accountability and comparative analyses.Comment: REVEAL 2018 Workshop on Offline Evaluation for Recommender System

Sequeval: A Framework to Assess and Benchmark Sequence-based Recommender Systems

In this paper, we present sequeval, a software tool capable of performing the offline evaluation of a recommender system designed to suggest a sequence of items. A sequence-based recommender is trained considering the sequences already available in the system and its purpose is to generate a personalized sequence starting from an initial seed. This tool automatically evaluates the sequence-based recommender considering a comprehensive set of eight different metrics adapted to the sequential scenario. sequeval has been developed following the best practices of software extensibility. For this reason, it is possible to easily integrate and evaluate novel recommendation techniques. sequeval is publicly available as an open source tool and it aims to become a focal point for the community to assess sequence-based recommender systems.Comment: REVEAL 2018 Workshop on Offline Evaluation for Recommender System

Semantic annotation of medical documents in CDA context

The goal of this work is to recover semantic and structural information from medical documents in electronic format. Despite the progressive diffusion of Electronic Health Record systems, a lot of medical information, also for legacy reasons, is available to patients and physicians in image-only or textual format. The difficulties of obtaining such information when needed result in high costs for health providers. In this work we develop the concept of a system designed to convert legacy medical documents into a standard and interoperable format compliant with the Clinical Document Architecture model by the means of semantic annotation

Visualizing ratings in recommender system datasets

The numerical outcome of an offline experiment involving different recommender systems should be interpreted also considering the main characteristics of the available rating datasets. However, existing metrics usually exploited for comparing such datasets like sparsity and entropy are not enough informative for reliably understanding all their peculiarities. In this paper, we propose a qualitative approach for visualizing different collections of user ratings in an intuitive and comprehensible way, independently from a specific recommendation algorithm. Thanks to graphical summaries of the training data, it is possible to better understand the behaviour of different recommender systems exploiting a given dataset. Furthermore, we introduce RS-viz, a Web-based tool that implements the described method and that can easily create an interactive 3D scatter plot starting from any collection of user ratings. We compared the results obtained during an offline evaluation campaign with the corresponding visualizations generated from the HetRec LastFM dataset for validating the effectiveness of the proposed approach

Trasporto di carica in cristalli organici semiconduttori

Uno dei settori che più si stanno sviluppando nell'ambito della ricerca applicata è senza dubbio quello dell'elettronica organica. Nello specifico lo studio è sospinto dagli indubbi vantaggi che questi dispositivi porterebbero se venissero prodotti su larga scala: basso costo, semplicità realizzativa, leggerezza, flessibilità ed estensione. È da sottolineare che dispositivi basati su materiali organici sono già stati realizzati: si parla di OLED (Organic Light Emitting Diode) LED realizzati sfruttando le proprietà di elettroluminescenza di alcuni materiali organici, OFET (Organig Field Effect Transistor) transistor costruiti con semiconduttori organici, financo celle solari che sfruttano le buone proprietà ottiche di questi composti. Oggetto di analisi di questa tesi è lo studio delle proprietà di trasporto di alcuni cristalli organici, al fine di estrapolarne la mobilità intrinseca e verificare come essa cambi se sottoposti a radiazione x. I due cristalli su cui si è focalizzata questa trattazione sono il 1,5-Dinitronaphtalene e il 2,4-Dinitronaphtol; su di essi è stata eseguita una caratterizzazione ottica e una elettrica, in seguito interpretate con il modello SCLC (Space Charge Limited Current). I risultati ottenuti mostrano che c'è una differenza apprezzabile nella mobilità nei due casi con e senza irraggiamento con raggi x

All you need is ratings: A clustering approach to synthetic rating datasets generation

The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems. However, the number of rating datasets is limited because of the costs required for their creation and the fear of violating the privacy of the users by sharing them. For this reason, numerous research attempts investigated the creation of synthetic collections of ratings using generative approaches. Nevertheless, these datasets are usually not reliable enough for conducting an evaluation campaign. In this paper, we propose a method for creating synthetic datasets with a configurable number of users that mimic the characteristics of already existing ones. We empirically validated the proposed approach by exploiting the synthetic datasets for evaluating different recommenders and by comparing the results with the ones obtained using real datasets

SemRevRec: a recommender system based on user reviews and linked data

Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users' decision processes. In this paper we propose a novel recommendation approach based on the semantic annotation of entities mentioned in user reviews and on the knowledge available in the Web of Data. We compared our recommender system with two baseline algorithms and a state-of-the-art Linked Data based approach. Our system provided more diverse recommendations with respect to the other techniques considered, while obtaining a better accuracy than the Linked Data based method

Ultrafast spectroscopy of metal halide perovskites and III-V semiconductors

A key phenomenon to improve even further the efficiency of photovoltaic technology is hot carrier dynamics: the possibility of harvesting hot, energetic carriers in photovoltaics could increase the efficiency of such devices beyond the Shockley-Queisser limit. In this thesis advances in the understanding of hot carrier dynamics in metal halide perovskite semiconductors (MHP) are reported: the influence of the composition on the hot carrier cooling process was investigated by means of optical pump terahertz probe spectroscopy (OPTP) and transient absorption spectroscopy (TA). In particular the role of the metal in controlling the electron-phonon coupling and phonon-phonon coupling was studied. A new phenomenological model aimed at describing the cooling dynamics of hot carriers is introduced, and its parameter linked to the microscopical physical processes underlying energy relaxation. A fully inorganic tin-based perovskite semiconductor is studied using OPTP and the hot carrier cooling time connected to the first stage of cooling is measured, compared to the prototypical III-V semiconductor GaAs, and linked to the Fröhlich electron-LO phonon interaction. The influence of the bandstructure on the dynamics is also assessed. A series of mixed lead-tin perovskites with controlled Pb/Sn ratio is investigated by both OPTP and TA spectroscopy. The numerical outcome of the two techniques is investigated and the differences linked to the different physical processes the two techniques probe, helping clarify discrepancies that appeared in previous works on the subject. The influence of the metal fraction on the cooling dynamic is also established, and linked to the modification of the electron-phonon and phonon-phonon interaction caused by alloying. Finally the simpler, narrow-gap semiconductor InSb is studied for the first time via terahertz cyclotron spectroscopy. The effective mass of InSb was measured during the cooling of hot carriers towards the band edge, finding that the change in effective mass during this process is indeed measurable and can be described by a simple model assuming a nonparabolic band dispersion. These findings suggest possible new directions for the design and implementation of future of future semiconductor materials and devices with optimised carrier cooling profiles