Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks

Information garnered from activity on location-based social networks can be harnessed to characterize urban spaces and organize them into neighborhoods. In this work, we adopt a data-driven approach to the identification and modeling of urban neighborhoods using location-based social networks. We represent geographic points in the city using spatio-temporal information about Foursquare user check-ins and semantic information about places, with the goal of developing features to input into a novel neighborhood detection algorithm. The algorithm first employs a similarity metric that assesses the homogeneity of a geographic area, and then with a simple mechanism of geographic navigation, it detects the boundaries of a city's neighborhoods. The models and algorithms devised are subsequently integrated into a publicly available, map-based tool named Hoodsquare that allows users to explore activities and neighborhoods in cities around the world. Finally, we evaluate Hoodsquare in the context of a recommendation application where user profiles are matched to urban neighborhoods. By comparing with a number of baselines, we demonstrate how Hoodsquare can be used to accurately predict the home neighborhood of Twitter users. We also show that we are able to suggest neighborhoods geographically constrained in size, a desirable property in mobile recommendation scenarios for which geographical precision is key.Comment: ASE/IEEE SocialCom 201

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science.

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective 'whole-of-enterprise' data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where 'enterprise big tables' are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science

A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach's effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on

Managing cyber risk in organizations and supply chains

In the Industry 4.0, modern organizations are characterized by an extensive digitalization and use of Information Technology (IT). Even though there are significant advantages in such a technological progress, a noteworthy drawback is represented by cyber risks, whose occurrence dramatically increased over the last years. The information technology literature has shown great interested toward the topic, identifying mainly technical solutions to face these emerging risks. Nonetheless, cyber risks cause business disruption and damages to tangible and intangible corporate assets and require a major integration between technical solutions and a strategic management. Recently, the risk management domain and the supply chain literature have provided studies about how an effective cyber risk management process should be planned, to improve organizational resilience and to prevent financial drawbacks. However, the aforementioned studies are mainly theoretical and there is still a significant lack of empirical studies in the management literature, measuring the potential effects of cyber threats within single companies, and along networks of relationships, in a wider supply chain perspective. The present thesis aims at filling some of these gaps through three empirical essays. The first study has implemented a Grounded Theory approach to develop an interview targeting 15 European organizations. Afterwards, the fuzzy set Qualitative Comparative Analysis (fsQCA) has been performed, in order to ascertain how managers perceive cyber risks. Results contradict studies that focus merely on technical solution, and con\ufb01rm the dynamic capability literature, which highlights the relevance of a major integration among relational, organizational, and technical capabilities when dealing with technological issues. Moreover, the study proposes a managerial framework that draws on the dynamic capabilities view, in order to consider the complexity and dynamism of IT and cyber risks. The framework proposes to implement both technical (e.g. software, insurance, investments in IT assets) and organizational (e.g. team work, human IT resources) capabilities to protect the capability of the company to create value. The second essay extends the investigation of the drawbacks of cyber risks to supply chains. The study conducts a Grounded Theory empirical investigation toward several European organizations that rely on security and risk management standards in order to choose the drivers of systematic IT and cyber risk management (risk assessment, risk prevention, risk mitigation, risk compliance, and risk governance). The evidence gleaned from the interviews have highlighted that investments in supply chain mitigation strategies are scant, resulting in supply chains that perform like they had much higher risk appetite than managers declared. Moreover, it has emerged a general lack of awareness regarding the effects that IT and cyber risks may have on supply operations and relationships. Thus, a framework drawing on the supply chain risk management is proposed, offering a holistic risk management process, in which strategies, processes, technologies, and human resources should be aligned in coherence with the governance of each organization and of the supply chain as a whole. The \ufb01nal result should be a supply chain where the actors share more information throughout the whole process, which guarantees strategic bene\ufb01ts, reputation protection, and business continuity. The third essay draws on the Situational Crisis Communication Theory (SCCT) to ascertain whether and how different types of cyber breaches differently affect the corporate reputation, defined as a multidimensional construct in which perceptions of customers, suppliers, (potential) employees, investors and local communities converge. Data breaches have been categorized into three groups by the literature, meaning intentional and internal to the organization (e.g., malicious employees stealing customers\u2019 data), unintentional and internal to the organization (e.g., incorrect security settings that expose private information), and intentional and external to the organization (e.g., ransomware infecting companies\u2019 software). However, this is among the first study to analyse the different reputational drawbacks these types may cause. Moreover, the study considers that, in the industry 4.0 era, social media analysis may be of paramount importance for organizations to understand the market. In fact, user-generated content (UGC), meaning the content created by users, might help in understanding which dimensions of the corporate have been more attacked after a data breach. In this context, the study implements the Latent Dirichlet Allocation (LDA) automated method, a base model in the family of \u201ctopic models\u201d, to extract the reputational dimensions expressed in UGC of a sample of 35 organizations in nine industries that had a data breach incident between 2013 and 2016. The results reveal that in general, after a data breach, three dimensions\u2014perceived quality, customer orientation and corporate performance\u2014 are a subject of debate for users. However, if the data breach was intentional ad malicious, users focused more on the role of firms\u2019 human resources management, whereas if users did not identify a responsible, users focused more on privacy drawbacks. The study complements crisis communication research by categorizing, in a data breach context, stakeholders\u2019 perceptions of a crisis. In addition, the research is informative for risk management literature and reputation research, analysing corporate reputation dimensions in a data breach crisis setting