In the mobile tourism domain, travel recommendation is a crucial task that aims to assist tourists in finding relevant travel-related services based on their specific contextual situation. In this thesis, in particular, we focus on two main dimensions of the user’s current context: (i) the spatio-temporal context, and (ii) the cognitive context, i.e., represented by the user’s topical interests. With the improvement of context-aware technologies, a big amount of contextual factors can be automatically gathered. However, not all of them are equally important for providing effective recommendations. Hence, it is imperative to identify and collect only those factors that truly affect the user’s preferences and improve the system effectiveness. For this reason, as a first aim of this thesis, we proposed an effective method for gathering and modeling relevant contextual factors. Our method, based on a selective context acquisition procedure, deems a contextual factor as relevant if it improves the system accuracy. We extensively explored the proposed method in the context of mobile Web search. Another issue that has been considered in this thesis concerns the fact that traditional approaches for personalized recommendation make use of a variety of user’s historical behaviors on the Web to define the user’s interests. This kind of information may be affected by noise, while reliable sources to model the user’s topical interests are absolutely necessary to have accurate recommendations. For this reason, in this thesis we focused on the extraction of the user’s topical interests from on-line User-Generated Content. More specifically, we proposed a multi-layer user profile, where each layer represents the user’s preferences with respect to a distinct travel-related service category. We used statistical language models to model the different layers. This model enables us to depict the probability distribution of words within a user’s language that s/he employs over social media in form of textual reviews. The expressive nature of the user profile was explored for the travel-related services recommendation in a restricted geographical area. Furthermore, based on the considered contextual factors, both spatio-temporal and cognitive, we proposed a context-aware and content-based approach for travel recommendation. This approach jointly leverages contextual factors and the user’s topical interests represented by her/his user profile to recommend travel-related services. First, a contextual pre-filtering is applied to estimate the relevance of travel-related services with respect to the user’s spatiotemporal context. Then, a novel Content-Based Filtering approach is performed on top of the selected travel-related services. The CBF approach considers both the user’s profile – as modeled in this thesis – and the travel-related services’ profile, and compare them by means of suitable similarity measures in order to recommend to the user the top-k services that are more similar to the user’s interests. The user profile and the context-aware and content-based recommendation approach proposed in this thesis have been employed in a mobile application that has been developed as a further contribution of this thesis. In particular, we designed a mobile user-friendly recommender system, namely LOOKER, which leverages travel recommendations to a mobile user who is visiting a new city. Moreover, LOOKER addresses the improvement of the user experience through an active learning of users preferences and interactive recommendation. The application was implemented as a Rich Mobile Application, within the PASRI project funded by European Union. We conducted a user study that allows to measure the user’s satisfaction and attitude towards our system. The results demonstrate that our context-aware and content-based approach can increase the recommendation accuracy while improve the user’s experience.

Nel dominio del turismo mobile, turisti nella ricerca di servizi correlati ai viaggi in base alla loro specifica situazione contestuale. In questa tesi, in particolare, ci concentriamo sulle due dimensioni principali del contesto attuale dell'utente: (i) il contesto spazio-temporale e (ii) il contesto cognitivo, cioè rappresentato dall'attualità dell'utente interessi.Con il miglioramento delle tecnologie context-aware, una grande quantità di fattori contestuali (tempo, luogo, ora, umore o compagno) possono essere raccolti automaticamente. Tuttavia, non tutti sono ugualmente importanti per fornire raccomandazioni efficaci. quindi, è imperativo identificare e raccogliere solo quei fattori che influenzano veramente le preferenze dell'utente e migliorare l'efficacia del sistema. Per questo motivo, come primo obiettivo di questa tesi, abbiamo proposto metodo efficace per la raccolta e la modellazione. Il nostro metodo, basato in una procedura di acquisizione del contesto selettivo, considera un fattore contestuale come pertinente se migliora la precisione del sistema. Abbiamo ampiamente esplorato il metodo proposto nel contesto del Web mobile ricerca. Un altro problema che è stato considerato in questa tesi approcci per la consulenza personalizzata sul Web (registri di ricerca, dati click-through) per definire gli interessi dell'utente. Questo tipo di le informazioni possono essere influenzate dal rumore, mentre fonti attendibili modellano gli interessi attuali dell'utente sono assolutamente necessari per avere raccomandazioni precise. Per questo motivo, in questa tesi noi focalizzato sull'estrazione degli interessi attuali dell'utente dai contenuti generati dagli utenti on-line. Più specificamente, abbiamo proposto un profilo utente multistrato, in cui ogni livello rappresenta il preferenze dell'utente in relazione a una distinta categoria di servizi correlati ai viaggi. Abbiamo usato il linguaggio statistico. Questo modello ci consente di descrivere la distribuzione di probabilità delle parole all'interno della lingua di un utente su cui si basa social media in forma di recensioni testuali. La natura espressiva del profilo utente è stata esplorata per i servizi relativi ai viaggi. Inoltre, sulla base dei fattori contestuali considerati, sia spazio-temporali che cognitivi, abbiamo proposto un approccio basato sul contesto e sul contenuto per le raccomandazioni di viaggio. Questo approccio sfrutta i fattori contestuali e gli interessi attuali dell'utente rappresentati dal suo profilo utente. Innanzitutto, un pre-filtro contestuale viene applicato per stimare la rilevanza dei servizi relativi ai viaggi rispetto a quelli spazio-temporali dell'utente contesto. Quindi, un nuovo approcio di filtraggio basato sul contenuto viene svolto in aggiunta i servizi di viaggio selezionati. L'approcio di filtraggio basato sul contenuto considera sia il profilo dell'utente che come modellato in questa tesi - e il profilo dei servizi relativi ai viaggi, e confrontarli per mezzo di misure di similarità adeguate per raccomandare all'utente i servizi top-k che sono di più simile agli interessi dell'utente. Il profilo utente e l'approccio basato sul contesto e sul contenuto offerto in questa tesi sono stati sviluppati come ulteriore contributo di questa tesi. In particolare, abbiamo progettato un sistema di raccomandazione mobile di facile utilizzo, ovvero LOOKER, che fa leva sui consigli di viaggio su un cellulare utente che sta visitando una nuova città. Inoltre, LOOKER affronta il miglioramento dell'utente esperienza attraverso una conoscenza attiva degli utenti. Il l'applicazione è stata implementata come Rich Mobile Application, all'interno del progetto PASRI finanziato dall'Unione Europea. Conduciamo uno studio dell'utente che consente di misurare la soddisfazione e l'attitudine dell'utente il nostro sistema. I risultati dimostrano che il nostro approccio basato sul contesto e sul contenuto può migliorare l'esperienza dell'utente.

(2018). Context-Aware Approaches to Mobile Search. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2018).

Context-Aware Approaches to Mobile Search

MISSAOUI, SONDESS
2018

Abstract

In the mobile tourism domain, travel recommendation is a crucial task that aims to assist tourists in finding relevant travel-related services based on their specific contextual situation. In this thesis, in particular, we focus on two main dimensions of the user’s current context: (i) the spatio-temporal context, and (ii) the cognitive context, i.e., represented by the user’s topical interests. With the improvement of context-aware technologies, a big amount of contextual factors can be automatically gathered. However, not all of them are equally important for providing effective recommendations. Hence, it is imperative to identify and collect only those factors that truly affect the user’s preferences and improve the system effectiveness. For this reason, as a first aim of this thesis, we proposed an effective method for gathering and modeling relevant contextual factors. Our method, based on a selective context acquisition procedure, deems a contextual factor as relevant if it improves the system accuracy. We extensively explored the proposed method in the context of mobile Web search. Another issue that has been considered in this thesis concerns the fact that traditional approaches for personalized recommendation make use of a variety of user’s historical behaviors on the Web to define the user’s interests. This kind of information may be affected by noise, while reliable sources to model the user’s topical interests are absolutely necessary to have accurate recommendations. For this reason, in this thesis we focused on the extraction of the user’s topical interests from on-line User-Generated Content. More specifically, we proposed a multi-layer user profile, where each layer represents the user’s preferences with respect to a distinct travel-related service category. We used statistical language models to model the different layers. This model enables us to depict the probability distribution of words within a user’s language that s/he employs over social media in form of textual reviews. The expressive nature of the user profile was explored for the travel-related services recommendation in a restricted geographical area. Furthermore, based on the considered contextual factors, both spatio-temporal and cognitive, we proposed a context-aware and content-based approach for travel recommendation. This approach jointly leverages contextual factors and the user’s topical interests represented by her/his user profile to recommend travel-related services. First, a contextual pre-filtering is applied to estimate the relevance of travel-related services with respect to the user’s spatiotemporal context. Then, a novel Content-Based Filtering approach is performed on top of the selected travel-related services. The CBF approach considers both the user’s profile – as modeled in this thesis – and the travel-related services’ profile, and compare them by means of suitable similarity measures in order to recommend to the user the top-k services that are more similar to the user’s interests. The user profile and the context-aware and content-based recommendation approach proposed in this thesis have been employed in a mobile application that has been developed as a further contribution of this thesis. In particular, we designed a mobile user-friendly recommender system, namely LOOKER, which leverages travel recommendations to a mobile user who is visiting a new city. Moreover, LOOKER addresses the improvement of the user experience through an active learning of users preferences and interactive recommendation. The application was implemented as a Rich Mobile Application, within the PASRI project funded by European Union. We conducted a user study that allows to measure the user’s satisfaction and attitude towards our system. The results demonstrate that our context-aware and content-based approach can increase the recommendation accuracy while improve the user’s experience.
PASI, GABRIELLA
BANDINI, STEFANIA
VIVIANI, MARCO
recommendation;; mobile;; context;; personalization;; tourism
recommendation;; mobile;; context;; personalization;; tourism
INF/01 - INFORMATICA
English
6-apr-2018
INFORMATICA - 87R
30
2016/2017
UNIVERSITY OF TUNIS - UNIVERSITÉ DE TUNIS
open
(2018). Context-Aware Approaches to Mobile Search. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2018).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/195639
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