Short-term rentals and transformations in urban areas: the case of Turin (Italy)

The last few years have been dominated by the spread of digital platforms allowing to connect the offer and demand of goods and services, facilitating either the rise or growth of original economic trends. An example is represented by digitally-enabled peer-to-peer accommodation systems that serve as intermediaries between people willing to rent for short-term periods rooms or entire residential units and travellers preferring to stay in private houses rather than in hotels or other hospitality venues. Short-term rentals represent an opportunity of growth for cities and territories at large, but they are not neutral and related socio-economic effects may vary depending on both the context and the actors involved. The analysis of the characteristics of the phenomenon, including its diachronic evolution and its spatial distribution in urban contexts, may represent a fundamental step to better understand a variety of possible consequences, and it could also inform decision-making and regulations at the local level. The goal of this article is to analyse the characteristics and spread of short-term rental accommodations in Turin (Italy), experimenting the adoption of the Microzones identified by the Turin Real Estate Market Observatory as spatial unit of analysis; more particularly, this approach aims to identify which are the areas of the city that have been particularly interested by this phenomenon, then allowing to suggest possible implications and new research horizons

Affitti brevi e trasformazioni nelle aree urbane: il caso di Torino

Negli ultimi anni si è assistito alla diffusione di piattaforme digitali in grado di mettere in contatto la domanda e l’offerta di beni e servizi, le quali hanno contribuito ad alimentare fenomeni economici prima inesistenti o decisamente più limitati. È questo il caso dei siti web che svolgono la funzione di intermediari fra coloro che desiderano proporre in affitto stanze o intere unità immobiliari e coloro che, in alternativa agli hotel o ad altre strutture ricettive, preferiscono soggiornare in stanze o case di privati. Questa realtà rappresenta un’occasione di sviluppo per città e territori, ma è lungi dall’essere neutra e gli effetti socioeconomici ad essa collegati possono variare in base agli attori coinvolti e ai diversi contesti in cui ha luogo. L’adozione di una prospettiva di analisi che tenga in considerazione non solo le caratteristiche e l’evoluzione del fenomeno, ma anche la sua articolazione spaziale all’interno delle singole realtà urbane può rappresentare un passo fondamentale per meglio comprendere le eventuali conseguenze a esso associate e orientare politiche di gestione e regolamentazione coerenti con gli obiettivi di crescita locale. Con questo contributo - applicato all’evidenza di Torino- si vuole proporre un approccio metodologico incentrato sull’analisi degli affitti brevi in relazione alle Microzone censuarie, al fine di comprendere meglio quali siano le aree della città fino ad ora maggiormente interessate dal fenomeno e suggerire possibili implicazioni e orizzonti di ricerca

Representation learning in heterogeneous information networks for user modeling and recommendations

Doctor of PhilosophyDepartment of Computer ScienceWilliam H. HsuCurrent research in the field of recommender systems takes into consideration the interaction between users and items; we call this the homogeneous setting. In most real world systems, however these interactions are heterogeneous, i.e., apart from users and items there are other types of entities present within the system, and the interaction between the users and items occurs in multiple contexts and scenarios. The presence of multiple types of entities within a heterogeneous information network, opens up new interaction modalities for generating recommendations to the users. The key contribution of the proposed dissertation is representation learning in heterogeneous information networks for the recommendations task. Query-based information retrieval is one of the primary ways in which meaningful nuggets of information is retrieved from large amounts of data. Here the query is represented as a user's information need. In a homogeneous setting, in the absence of type and contextual side information, the retrieval context for a user boils down to the user's preferences over observed items. In a heterogeneous setting, information regarding entity types and preference context is available. Thus query-based contextual recommendations are possible in a heterogeneous network. The contextual query could be type-based (e.g., directors, actors, movies, books etc.) or value-based (e.g., based on tag values, genre values such as ``Comedy", ``Romance") or a combination of Types and Values. Exemplar-based information retrieval is another technique for of filtering information, where the objective is to retrieve similar entities based on a set of examples. This dissertation proposes approaches for recommendation tasks in heterogeneous networks, based on these retrieval mechanisms present in traditional information retrieval domain