Pasquale Lops

• University of Bari Aldo Moro

In the digital era, Recommender Systems are a crucial component, commonly used in services such as music and movie streaming. Despite their widespread adoption, surprisingly little attention has been devoted to developing systems that can positively impact users' well-being and health. In an effort to combat the negative effects of a sedentary way...

In this paper, we present a knowledge-aware recommendation model based on neuro-symbolic graph embeddings that encode first-order logic rules. Our approach is based on the intuition that is the basis of neuro-symbolic AI systems: to combine deep learning and symbolic reasoning in one single model, in order to take the best out of both the paradigms...

In working environments where prolonged sitting is ubiquitous, maintaining correct posture is crucial to alleviating musculoskeletal problems and improving general well-being. This research presents an innovative approach for assessing sitting postures using any camera conveniently accessible to the subject, be it integrated into their personal com...

The 10th edition of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems was held as part of the 17th ACM Conference on Recommender Systems (RecSys), the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. The workshop was organized...

Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of b...

Digital Assistants are overgrowing in the mobile application industry and are now implemented in various commercial devices. So far, their use in the health domain is limited and often narrowed to remote monitoring of specific patient pathology. The main contribution of this paper is HELENA, a conversational agent endowed with healthcare knowledge...

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...

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...

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...

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...

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...

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...

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...

Question Answering (QA) over Knowledge Graphs (KGs) has gained its momentum thanks to the spread of the Semantic Web. However, despite the abundance of methods proposed in this field, there are still many aspects that need to be fully covered. One of them is the generation of SPARQL queries with modifiers, i.e. queries that are made up not only by...

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,...

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...

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.

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...

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.

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...

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...

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...

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...

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...

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...

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,...

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...

More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take in...

In this paper we introduce the concept of holistic user profile, intended as a unique representation of a user that merges the heterogeneous footprints she spread on social networks and through personal devices, and we present a framework that supports the creation of such user models. Our holistic user model is based on the insight that each perso...

In this paper we present Myrror, a platform that supports the creation of a unique representation of the user that encodes several facets such as her interests, activities, habits, mood, social connections and so on. Such a representation, that we called holistic user model, is based on the footprints the user spread on social networks and through...

In this paper we present a deep content-based recommender system (DeepCBRS) that exploits Bidirectional Recurrent Neural Networks (BRNNs) to learn an effective representation of the items to be recommended based on their textual description. Next, such a representation is extended by introducing structured features extracted from the Linked Open Da...

In this paper we introduce the concept of holistic recommendations, namely a set of suggestions generated by exploiting a more comprehensive representation of the user that relies on the personal information coming from different heterogeneous data sources (e.g., social networks, wristbands, smartphones, etc.) and considers the diverse relations an...

In this contribution we propose a hybrid recommendation framework based on classification algorithms such as Random Forests and Naive Bayes, which are fed with several heterogeneous groups of features. We split our features into two classes: classic features, as popularity-based, collaborative and content-based ones, and extended features gathered...

In this article we propose a framework that generates natural language explanations supporting the suggestions generated by a recommendation algorithm. The cornerstone of our approach is the usage of Linked Open Data (LOD) for explanation aims. Indeed, the descriptive properties freely available in the LOD cloud (e.g., the author of a book or the d...

In this article we propose a hybrid recommendation framework based on classification algorithms such as Random Forests and Naive Bayes, which are fed with several heterogeneous groups of features. We split our features into two classes: classic features, as popularity-based, collaborative and content-based ones, and extended features gathered from...

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selec...

In this paper we propose a multi-criteria recommender system based on collaborative filtering (CF) techniques, which exploits the information conveyed by users' reviews to provide a multi-faceted representation of users' interests. To this end, we exploited a framework for opinion mining and sentiment analysis, which automatically extracts relevant...

This article reports on the CBRecSys 2016 workshop, the third edition of the workshop on New Trends in Content-based Recommender Systems, co-located with RecSys 2016 in Boston, MA. Content-based recommendation has been applied successfully in many different domains, but it has not seen the same level of attention as collaborative filtering techniqu...

The recent spread of Linked Open Data (LOD) fueled the research in the area of Recommender Systems, since the (semantic) data points available in the LOD cloud can be exploited to improve the performance of recommendation algorithms by enriching item representations with new and relevant features.In this article we investigate the impact of the fea...

In this article we propose a hybrid recommendation framework based on classification algorithms as Random Forests and Naive Bayes. We fed the framework with several heterogeneous groups of features, and we investigate to what extent features gathered from the Linked Open Data (LOD) cloud (as the genre of a movie or the writer of a book)) as well as...

In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top-N content-based recommendation scenario. Specifically, we propose a deep architecture which adopts Long Short Term Memory (LSTM) networks to jointly learn two embeddings representing the items to be recommended as well as the preferences of the user. Next, g...

HealthNet (HN) is a social network that brings together patients with similar health conditions. HN helps users in finding a solution to their health problems by suggesting doctors and health facilities that best fit the patient profile. Indeed, the core component of HN is a recommender system that suggests patients similar to the target user and s...

Conversational recommender systems produce personalized recommendations of potentially useful items by utilizing natural language dialogues for detecting user preferences, as well as for providing recommendations. In this work we investigate the role of affective factors such as attitudes, emotions, likes and dislikes in conversational recommender...

In this article we investigate how the knowledge available in the Linked Open Data cloud (LOD) can be exploited to improve the effectiveness of a semantics-aware graph-based recommendation framework based on Personalized PageRank (PPR). In our approach we extended the classic bipartite data model, in which only user-item connections are modeled, by...

T-RecS is a system which implements several computational linguistic techniques for analyzing word usage variations over time periods in a document collection. We analyzed ACM RecSys conference proceedings from the first edition held in 2007, to the one held in 2015. The idea is to identify linguistic phenomena that reflect some interesting variati...

Thanks to the recent spread of the Linked Open Data (LOD) initiative, a huge amount of machine-readable knowledge encoded as RDF statements is today available in the so-called LOD cloud. Accordingly, a big effort is now spent to investigate to what extent such information can be exploited to develop new knowledge-based services or to improve the ef...

In the last decade, collaborative filtering approaches have shown their effectiveness in computing accurate recommendations starting from the user-item matrix. Unfortunately, due to their inner nature, collaborative algorithms work very well with dense matrices but show their limits when they deal with sparse ones. In these cases, encoding user pre...

The retrieval of pertaining information during the decision-making process requires more than the traditional concept of relevance to be fulfilled. This task asks for opinionated sources of information able to influence the user’s point of view about an entity or target. We propose SABRE, a Sentiment Aspect-Based Retrieval Engine, able to tackle th...

This paper presents T-RecS (Temporal analysis of Recommender Systems conference proceedings), a framework that supplies services to analyze the Recommender Systems Conference proceedings from the first edition, held in 2007, to the last one, held in 2015, under a temporal point of view. The idea behind T-RecS is to identify linguistic phenomena tha...

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selec...

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage...

In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through...

The growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. On one hand, the Web is becoming more and more multilingual, and on the other hand users themselves are becoming increasingly polyglot. In this context, platforms for intelligent information access as search en...

Community question answering (CQA) sites use a collaborative paradigm to satisfy complex information needs. Although the task of matching questions to their best answers has been tackled for more than a decade, the social question-answering practice is a complex process. The factors influencing the accuracy of question-answer matching are many and...

Recommender systems analyze a user’s past behavior, build a user profile that stores information about her interests, maybe find others who have a similar profile, and use that information to find potentially interesting items. The main limitation of this approach is that provided recommendations are accurate, because they match the user profile, b...

This paper presents the results of The Italian Hate Map, a research project aiming to monitor the level of intolerance of the Italian country by mining the content posted on social networks. Within the project, a pipeline of algorithms for data extraction, semantic processing, sentiment analysis and content classification has been defined to proces...

The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of recommender systems. In this paper we feed recommender systems with features coming from the Linked Open Data (LOD) cloud - a huge amount of machine-readable knowledge encoded as RDF statements - w...

In this paper we present a preliminary investigation towards the adoption of Word Embedding techniques in a content-based recommendation scenario. Specifically, we compared the effectiveness of three widespread approaches as Latent Semantic Indexing, Random Indexing and Word2Vec in the task of learning a vector space representation of both items to...

EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for the Italian language: since 2007 shared tasks have been proposed covering the analysis of both written and spoken language with the aim of enhancing the development and dissemination of resources and technologies for Italian. EVALITA is an initiative of the Itali...

In this paper we compare several techniques to automatically feed a graph-based recommender system with features extracted from the Linked Open Data (LOD) cloud. Specifically , we investigated whether the integration of LOD-based features can improve the effectiveness of a graph-based rec-ommender system and to what extent the choice of the feature...

Emotions play a crucial role in the decision making process. Frequently, choices are strongly influenced by the mood of the moment, and the same person could take different decisions at different time on the same topic. Recommender systems, that are definitively recognized as tools for supporting the decision making process, demonstrated to be more...

A conversational recommender system should interactively assist users in order to understand their needs and preferences and produce personalized recommendations accordingly. While traditional recommender systems use a single-shot approach, the conversational ones refine their suggestions during the conversation since they gain more knowledge about...

Wealth management services have become a priority for most financial services organizations firms. As investors are pressing wealth managers to justify their value proposition, turbulence in financial markets reinforced the need to improve the advisory offering with more customized and sophisticated services. This paper presents a framework that co...

If we assume that an important function of recommender systems is to help people make better choices, it follows that people who design and study recommender systems ought to have a good understanding of how people make choices and how human choice can be supported. This chapter starts with a compact synthesis of research on the various ways in whi...

In this work we present a semantic recommender system able to suggest doctors and hospitals that best fit a specific patient profile. The recommender system is the core component of the social network named HealthNet (HN). The recommendation algorithm first computes similarities among patients, and then generates a ranked list of doctors and hospit...

This paper presents a domain-agnostic framework for intelligent processing of textual streams coming from social networks. The framework implements a pipeline of techniques for semantic representation, sentiment analysis, automatic content classification, and provides an analytics console to get some findings from the extracted data. The effectiven...

This paper describes the techniques used to build a virtual player for the popular TV game “Who Wants to Be a Millionaire?”. The player must answer a series of multiple-choice questions posed in natural language by selecting the correct answer among four different choices. The architecture of the virtual player consists of 1) a Question Answering (...