4,955 research outputs found
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NLP Analysis and Recommendation System for Yelp
Yelp provides a valuable platform to share massive restaurant information, but it is difficult for its users to distinguish a relevant one among others. People are overwhelmed by multifarious information and unable to efficaciously glean germane information. Thus, it’s necessary to build a recommendation model which can filter and prioritize information, efficiently recommending appropriate restaurants to Yelp’s users, so the users can make correct decisions. Meanwhile, it can also help businesses to target their potential customers more accurately by sending similar-preference recommendations. This research explores different preferences and topics from Yelp restaurant reviews to understand characteristics of each user and restaurant, and then applies four practical algorithms to provide the most precise and personalized restaurant recommendations for the users
Supporting lay users in privacy decisions when sharing sensitive data
The first part of the thesis focuses on assisting users in choosing their privacy settings, by using machine learning to derive the optimal set of privacy settings for the user. In contrast to other work, our approach uses context factors as well as individual factors to provide a personalized set of privacy settings. The second part consists of a set of intelligent user interfaces to assist the users throughout the complete privacy journey, from defining friend groups that allow targeted information sharing; through user interfaces for selecting information recipients, to find possible errors or unusual settings, and to refine them; up to mechanisms to gather in-situ feedback on privacy incidents, and investigating how to use these to improve a user’s privacy in the future. Our studies have shown that including tailoring the privacy settings significantly increases the correctness of the predicted privacy settings; whereas the user interfaces have been shown to significantly decrease the amount of unwanted disclosures.Insbesondere nach den jüngsten Datenschutzskandalen in sozialen Netzwerken wird der Datenschutz für Benutzer immer wichtiger. Obwohl die meisten Benutzer behaupten Wert auf Datenschutz zu legen, verhalten sie sich online allerdings völlig anders: Sie lassen die meisten Datenschutzeinstellungen der online genutzten Dienste, wie z. B. von sozialen Netzwerken oder Diensten zur Standortfreigabe, unberührt und passen sie nicht an ihre Datenschutzanforderungen an. In dieser Arbeit werde ich einen Ansatz zur Lösung dieses Problems vorstellen, der auf zwei verschiedenen Säulen basiert. Der erste Teil konzentriert sich darauf, Benutzer bei der Auswahl ihrer Datenschutzeinstellungen zu unterstützen, indem maschinelles Lernen verwendet wird, um die optimalen Datenschutzeinstellungen für den Benutzer abzuleiten. Im Gegensatz zu anderen Arbeiten verwendet unser Ansatz Kontextfaktoren sowie individuelle Faktoren, um personalisierte Datenschutzeinstellungen zu generieren. Der zweite Teil besteht aus einer Reihe intelligenter Benutzeroberflächen, die die Benutzer in verschiedene Datenschutzszenarien unterstützen. Dies beginnt bei einer Oberfläche zur Definition von Freundesgruppen, die im Anschluss genutzt werden können um einen gezielten Informationsaustausch zu ermöglichen, bspw. in sozialen Netzwerken; über Benutzeroberflächen um die Empfänger von privaten Daten auszuwählen oder mögliche Fehler oder ungewöhnliche Datenschutzeinstellungen zu finden und zu verfeinern; bis hin zu Mechanismen, um In-Situ- Feedback zu Datenschutzverletzungen zum Zeitpunkt ihrer Entstehung zu sammeln und zu untersuchen, wie diese verwendet werden können, um die Privatsphäreeinstellungen eines Benutzers anzupassen. Unsere Studien haben gezeigt, dass die Verwendung von individuellen Faktoren die Korrektheit der vorhergesagten Datenschutzeinstellungen erheblich erhöht. Es hat sich gezeigt, dass die Benutzeroberflächen die Anzahl der Fehler, insbesondere versehentliches Teilen von Daten, erheblich verringern
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanaciĂł de persones (PRS) sĂłn un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanaciĂł de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecĂ rrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definiciĂł d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funciĂł de la naturalesa de la informaciĂł necessĂ ria. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procĂ©s, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implĂcita per tal de caracteritzar als usuaris des de mĂşltiples perspectives i punts de vista multidimensionals sense la influència explĂcita de l’usuari. Això s’aplica al problema d'assignaciĂł d’avaluadors per a articles de conferències. Es presta especial atenciĂł al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecĂ rrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les Ă rees d’interès per a caracteritzar les persones es dedueixen dels seus currĂculums i s’expressen en termes d’incertesa evitant que els interessos es demanin explĂcitament a les persones. El sistema s'aplica a un problema de selecciĂł de personal on es posa èmfasi en les preferències del candidat que condueixen a un procĂ©s d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informaciĂł implĂcita i atributs indicats explĂcitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que mantĂ© la informaciĂł original del conjunt de dades del clĂşster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les Ă rees d'aplicaciĂł futures i noves perspectives.Postprint (published version
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The art of spending and recommendations in personal finance
Happiness is one of the most important aspects of human lives, yet the literature on emotional well-being indicates that people often fail to correctly anticipate the hedonic consequences of future events. As a result, individuals end up being not as happy as they thought they would be. This phenomenon also applies to the domain of personal finance where people make bad decisions about purchases. In this paper, we identified a new opportunity for the research on recommender systems in personal finance and through analysis demonstrated that intelligent recommenders can help to minimize errors in affective forecasts and enhance happiness of people in the domain of consumption. Furthermore, we reviewed problems associated with design of such recommenders and proposed approaches to overcome them.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655723
Divertimi: A Tourist Guide to a Unique and Enriching Experience
This project lays a foundation for the development of an e-tourism website by Azienda di Promozione Turistica della Provincia di Venezia, the provincial tourism authority in the Veneto region of Italy. Our design employs individual and group profiling to recommend destinations and attractions. Social networking and various forms of user-generated narratives support travel recommendations. Finally, we propose a system for offering a personalized trip package based on user interests
WGLT Program Guide, October-November, 1998
This guide details programming for WGLT, a public radio station owned by Illinois State University.https://ir.library.illinoisstate.edu/wgltpg/1161/thumbnail.jp
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Report to Carnegie UK Trust and CILIP on a two-stage study of the Carnegie and Kate Greenaway Shadowing Scheme
The Chartered Institute of Library and Information Professionals (CILIP) organises the CILIP Carnegie and Kate Greenaway (CKG) Children’s Book Awards. CILIP also manages the accompanying CKG Shadowing Scheme and its associated website, which librarians and other group leaders and group members can use to support reading and foster young people’s enjoyment of reading. In order to explore the potential of this scheme, build on previous evaluations and make recommendations regarding development, two studies were commissioned in 2011 and 2012, funded by Carnegie UK Trust. The 2012 study built substantially on the research carried out in 2011 and is therefore better regarded as the second phase of a continuing project. The combined results of both phases are presented in this report
An Investigation of Gender Bias in a Career Assessment for a STEM Field
This study examined whether career counselors differ in their recommendations for a STEM (i.e., science, technology, engineering, mathematics) career (specifically, computer scientist) based on the gender of the client. In a randomized two group experimental design with a qualitative component, a fictitious student bio was created in order to understand the possible conceptualization differences seen between career counselors in regards to gender bias in the STEM fields. The primary research questions looked at whether participants were less likely to recommend the female student, compared to the male, to a STEM related career and to pursue graduate school. A secondary research question was utilized to investigate the thought process underlying counselors’ recommendations. Participants (n=129) ranged from ages 23-71, were primarily female (78.3%, n=101), had a Master’s degree in a helping profession (70.5%, n=91), and were White (75.2%, n=97). Results of the primary research questions through a one-way MANOVA were seen as non-significant (n=129), Wilks’ λ = .992, F (3, 125) = 0.353, p = 0.787, partial eta squared = .008. The qualitative themes identified in the participants’ responses for their reasoning for the “top 3” and “bottom 3” careers recommended were: Student Profile Components, Strong Interest Inventory Results, Assumed Student Traits & Activities, and Further Exploration Needed. Interpretation of these results shows that the use of a standardized measure provides a protective factor against the implicit gender bias typically seen in other areas of academia and the workplace for women. The follow-up question also revealed, that while the majority of participants showed no gender bias differences in their recommendations, they also failed to consider gender in the conceptualization of the student profile. This shows a “gender blind” component that does not follow the multicultural awareness approach that counselors are currently trained in and what is necessary if counselors desire to help support females interested in STEM. Future studies should investigate career counselor bias utilizing different STEM careers and possibly an in-person interaction in order to pull at different biases and more intersectional identity elements (i.e., race and gender)
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An Investigation into the Effect of Consumer Experience Tourism on Brand Loyalty and Purchasing Behavior
Brand loyalty and repeat purchase intentions are accepted as important and inextricably intertwined phenomena in contemporary marketing literature, with many studies pertaining to this area. In order to achieve customer loyalty, it is important for companies to create strong bonds between their products or brands and consumers. Consumer Experience Tourism (CET) has been increasingly used as a strategic marketing tool in an attempt to strengthen such bonds, particularly by producers of frequently purchased consumer staples such as food and beverages. With no studies to date identified as having tested the effects of CET on medium to long-term consumer brand loyalty and purchasing behavior, how such behavior differs from that of consumers who have had other non-CET experiential interactions with the product or brand, and indeed those consumers who have had no experiential interaction with the product or brand, companies have a dilemma in how to treat this activity. Should they treat it as a worthwhile marketing expense that will reap long-term rewards, or as a tourist activity that should either cover its costs or show a profit due to limited benefits?
This dissertation consists of three studies that investigate the effects of CET on brand loyalty and purchasing behavior. Study 1 obtained 415 valid surveys from CET visitors to a single winery, investigating perceived product quality, perceived service quality, and the effects of charging (versus not charging) on purchasing behavior, and found that under conditions of both highly perceived product quality and highly perceived service quality, there were no significant differences in purchasing behavior. Study 2 obtained 437 valid surveys from CET visitors to the winery of survey in Study 1 who had visited over a six-year period, as well as consumers of the brands who had not engaged in CET, and found significant differences in attitudinal brand loyalty but not in purchasing behavior. Study 3 attempted to replicate the effects of a CET using a Consumer Experience Event (CEE), with a pre-event tracking survey obtaining 74 valid responses, followed by a post-event tracking survey that obtained 51 valid responses. It was found that this type of experience remote from the brand home was able to replicate many of the CET attributes and effects. This research therefore extends CET as a theoretical construct and begins to resolve the CET marketer’s dilemma
The local evaluation of Knutsford’s Healthy Living Network: January 2006 – December 2006
This was an exploratory study designed to evaluate the Knutsford Healthy Living Network (KHLN), one of many Healthy Living Centres (HLC) in the UK. The HLC initiative aims to ensure people can achieve their optimum state of health and well-being. The project report aims to establish the reach of KHLN services by monitoring the level of service usage using a database designed to capture service activity
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