Giovanni Semeraro

Giovanni Semeraro
Università degli Studi di Bari Aldo Moro | Università di Bari · Department of Computer Science

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547
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Publications

Publications (547)
Preprint
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Preference elicitation is a crucial step for every recommendation algorithm. Traditional interaction strategies for eliciting users’ interests and needs range from button-based interfaces, where users have to select what they like among a set of fixed alternatives, to more recent conversational interfaces, where users have to reply to some question...
Conference Paper
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Sustainability reporting has become an annual requirement in many countries and for certain types of companies. Sustainability reports inform stakeholders about companies’ commitment to sustainable development and their economic, social, and environmental sustainability practices. However, the fact that norms and standards allow a certain discretio...
Article
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Conversational Recommender Systems have received widespread attention in both research and practice. They assist people in finding relevant and interesting items through a multi-turn conversation. The use of natural language interaction also allows users to express their preferences with more flexibility. However, these systems often have to work i...
Article
In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own u...
Article
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Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated thr...
Conference Paper
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The automated analysis of medical documents has grown in research interest in recent years as a consequence of the social relevance of the thematic and the difficulties often encountered with short and very specific documents. In particular, this fervent area of research has stimulated the development of several techniques of automatic document cla...
Article
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In this article, we present a framework to build post hoc natural language justifications that supports the suggestions generated by a recommendation algorithm. Our methodology is based on the intuition that reviews’ excerpts contain much relevant information that can be used to justify a recommendation; thus, we propose a black-box explanation str...
Conference Paper
Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of re...
Article
In this article we present a context-aware recommendation method that exploits graph-based data models and Personalized PageRank to provide users with recommendations. In particular, our approach extends the basic graph-based representation that relies on users and items nodes by introducing a third class of nodes, that is to say, context nodes, wh...
Article
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Decision making is the cognitive process of identifying and choosing alternatives based on preferences, beliefs, and degree of importance given by the decision maker to objects or actions. For instance, choosing which movie to watch is a simple, small-sized decision-making process. Recommender systems help people to make this kind of choices, usual...
Article
In recent years, there has been a significant increase in interest in lexical semantic change detection. Many are the existing approaches, data used, and evaluation strategies to detect semantic shifts. The classification of change words against stable words requires thresholds to label the degree of semantic change. In this work, we compare state-...
Chapter
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The necessity to know information about the real identity of an online subject is a highly relevant issue in User Profiling, especially for analysis from digital sources such as social media. The digital identity of a user does not always present explicit data about her offline life such as age, gender, work, and more. This problem makes the task o...
Article
Though there are currently no statistics offering a global overview of online hate speech, both social networking platforms and organisations that combat hate speech have recognised that prevention strategies are needed to address this negative online phenomenon. While most cases of online hate speech target individuals on the basis of ethnicity an...
Article
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In this article, we present a platform that allows the creation of a comprehensive representation of the user that we call a holistic user model (HUM). Such a representation is based on the intuition that users’ personal data take different forms and come from several heterogeneous sources. Accordingly, we designed a pipeline that: (1) extracts per...
Article
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In this article, we present HealthAssistantBot, an intelligent virtual assistant able to talk with patients in order to understand their symptomatology, suggest doctors, and monitor treatments and health parameters. In a simple way, by exploiting a natural language-based interaction, the system allows the user to create her health profile, to descr...
Article
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The paper describes the Web platform built within the project “Contro l’Odio”, for monitoring and contrasting discrimination and hate speech against immigrants in Italy. It applies a combination of computational linguistics techniques for hate speech detection and data visualization tools on data drawn from Twitter.It allows users to access a huge...
Article
Full-text available
Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as...
Article
Digital Assistants (DA) such as Amazon Alexa, Siri, or Google Assistant are now gaining great diffusion, since they allow users to execute a wide range of actions through messages in natural language. Even though DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they do not yet implement recommendation faci...
Article
Full-text available
Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and ofte...
Article
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Natural Language Processing tasks recently achieved considerable interest and progresses following the development of numerous innovative artificial intelligence models released in recent years. The increase in available computing power has made possible the application of machine learning approaches on a considerable amount of textual data, demons...
Conference Paper
Full-text available
The paper describes the Web platform built within the project "Contro l'odio", for monitoring and contrasting discrimination and hate speech against immigrants in Italy. It applies a combination of computational linguistics techniques for hate speech detection and data visualization tools on data drawn from Twitter. It allows users to access a huge...
Chapter
Graph-based recommendation methods represent an established research line in the area of recommender systems. Basically, these approaches provide users with personalized suggestions by modeling a bipartite graph that connects the users to the items they like and exploit such connections to identify items that are interesting for the target user.
Conference Paper
Full-text available
Recent scientific studies on natural language processing (NLP) report the outstanding effectiveness observed in the use of context-dependent and task-free language understanding models such as ELMo, GPT, and BERT. Specifically, they have proved to achieve state of the art performance in numerous complex NLP tasks such as question answering and sent...
Conference Paper
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In this paper we present HATECHECKER, a tool for the automatic detection of hater users in online social networks which has been developed within the activities of "Contro L'Odio" research project. In a nutshell, our tool implements a methodology based on three steps: (i) all the Tweets posted by a target user are gathered and processed. (ii) senti...
Article
Full-text available
Query auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web-scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestio...
Chapter
We live in a time characterized by the continuous and massive production of textual and personal data, shared on Web platforms like Facebook, LinkedIn, Twitter, Wikipedia, and so on. These data often reveal very valuable information for those systems that offer an intelligent and personalized information access, such as personalized search engines,...
Chapter
The importance of content-based features in intelligent information access systems as search engines, information filtering tools, and recommender systems has been thoroughly discussed in the Introduction of this book. All the examples we have provided showed that textual data can be really useful to: (i) tackle some of the issues that affect data...
Chapter
In this chapter, we introduce a variety of techniques for endogenous semantics representation of textual content. Such techniques, also defined as distributional semantics methods, are based on the idea that the meaning of a word can be inferred by analyzing its distribution in the context of ordinary and concrete language usage.
Chapter
In the introduction of this book, we have thoroughly discussed the importance of adaptive and personalized systems in a broad range of applications. In particular, we have motivated the use of content-based information and textual data, and we have analyzed all the possible limitations of approaches based on keyword-based representation. In this ch...
Chapter
In this chapter, we introduce a different vision of the concept of semantics, since we will present a variety of techniques that allow to build a semantics-aware representation without the need of large corpora of textual data that are mandatory for endogenous semantics representation methodologies.
Conference Paper
Full-text available
In this paper we present a methodology to justify recommendations that relies on the information extracted from users' reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. T...
Conference Paper
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As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs fr...
Conference Paper
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The number of accounts that autonomously publish contents on the web is growing fast, and it is very common to encounter them, especially on social networks. They are mostly used to post ads, false information, and scams that a user might run into. Such an account is called bot, an abbreviation of robot (a.k.a. social bots, or sybil accounts). In o...
Conference Paper
Full-text available
Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden r...
Article
Recently, several methods have been proposed for introducing Linked Open Data (LOD) into recommender systems. LOD can be used to enrich the representation of items by leveraging RDF statements and adopting graph-based methods to implement effective recommender systems. However, most of those methods do not exploit embeddings of entities and relatio...
Conference Paper
Full-text available
User profiling is becoming increasingly holistic by including aspects of the user that until a few years ago seemed irrelevant. The content that users produce on the Internet and social networks is an essential source of information about their habits, preferences, and behaviors in many situations. One factor that has proved to be very important fo...
Conference Paper
Full-text available
The broad diffusion over the Internet of songs streaming services points out the need for implementing efficient and personalized strategies for incrementing the fidelity of the customers. This scenario can collect enough information about the user and the items for successfully design a Recommender System for the automatic continuation of playlist...
Conference Paper
In this paper we present a semantics-aware recommendation strategy that uses graph embedding techniques to learn a vector space reresentation of the items to be recommended. Such a representation relies on the tripartite graph which connects users, items and entities gathered from DBpedia, thus it encodes both collaborative and content-based inform...
Chapter
Full-text available
Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explo...
Article
In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framework consists of a meta-controller, which receives the...
Conference Paper
It is our great pleasure to welcome you to the UMAP 2019 Workshop on Explainable and Holisitic User Modeling (ExHUM). Our workshop took inspiration from the analysis of the recent Web dynamics: according to a recent claim by IBM, 90% of the data available today have been created in the last two years. Such an exponential growth of personal informat...
Book
This monograph gives a complete overview of the techniques and the methods for semantics-aware content representation and shows how to apply such techniques in various use cases, such as recommender systems, user profiling and social media analysis. Throughout the book, the authors provide an extensive analysis of the techniques currently proposed...
Article
Full-text available
Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this domain, by exploiting recommender systems that automatically compile lists of novel and diverse video a...
Chapter
Full-text available
Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. Generally, they have an interaction with users based on natural language, buttons, or both. In this paper we study the user interaction with a content-based recommender system implemented as a Telegram chatbot. More specifically,...