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 recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, 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 definició 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 funció de la naturalesa de la informació necessària. 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 procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version

Business Plan and Analysis for uplifting Small Business (SB)

A business plan plays a vital responsibility in the Company\u27s well-being due to the high impact on profitability, stability, and situation in the market. A robust and detailed plan provides a clear road map for the future, forces us to deliberate through the validity of a business idea, and can better understand the business\u27s financial data and the competition (Ashe-Edmunds, 2016). This project develops a business plan for Ruby Enterprise LLC for launching their first product Amma Genie, which supports small businesses and links consumers to purchase products from their local stores. As businesses move towards data and AI, the Company\u27s product will dramatically alter the way businesses operate by showing immense changes in customer service, employee-facing services, sales, and more

Customer Preferences for Restaurant Technology Innovations

When restaurateurs evaluate whether to adopt technology-based service innovations, they must consider not only the costs and benefits of that technology, but also customers’ reactions to the procedural changes accompanying the innovation. Technology that damages customer satisfaction may not be worthwhile, no matter how much it reduces labor costs. In this report we present the results of a national survey on customers’ perceptions of eleven restaurant technologies, as well as whether respondents use those technologies and the value they see in them. The technologies are pagers for table management, handheld order taking while waiting in line, internet-based ordering, kiosk-based payment, kiosk-based food ordering, online reservations, payment via SMS or text message, payment via (RFID) smart card, payment via cell phone using NFC technology, virtual menus available tableside with nutritional information, and virtual menus online with nutritional information. These technologies are categorized in the following five categories: kiosk, menu, online usage, payment-based service innovations, and queuing. Using a research technique called best-worst choice analysis, the study found that the technologies used most commonly were pagers and online reservations, while cell-phone payment was used hardly at all. The results show that the perceived value of a specific technology increases after the customers have had the opportunity to use it, and different demographic segments valued the technologies differently. Frequent technology users visited restaurants more often than infrequent technology users did

The Effects of Household Socio-demographics on Restaurant Threshold Prices

This study examines the determinants of a household’s threshold price for a restaurant meal cost increase; the level of cost increase that would cause households to either eat in restaurants less frequently or change what they would typically purchase. The design of the study is formulated using a Tobit model to examine the threshold price by differing social, economic and demographic characteristics of households in Kentucky as well as their preferences for restaurant-specific characteristics. The empirical estimates suggest that households that frequently have dinner at restaurants, households with higher incomes and households that strongly prefer full-service restaurants have a positive threshold price-range; which suggests that such households are more willing to pay an additional cost increase in restaurant meals. Conversely, households that always notice taxes before paying their checks, households close to retirement-age, and households that do not strongly prefer local-food restaurants have negative threshold price-range and are consequently less willing to pay an additional cost increase in restaurant meals

Research in Supply Chain Management: Issue and Area Development

Today the study of supply chain management (SCM) is growing rapidly and provides a great opportunity to do research both empirical and theoretical development. Research opportunities in SCM has been reviewed by many researchers and grouped into many categories. This paper contains a review of research SCM and classify into 7 categories, namely (1) SCM Operational Management & Strategy, (2) knowledge management, (3) Relationship Management, (4) Information Technology in SCM, (5) Supply Chain Design, Logistics & Infrastructure, (6) Global Issues, (7) Environment, Legal & Regulations. The issue in each category and research opportunities will be discussed in this paper. Keywords: Supply Chain Management, Research Opportunities in SCM, Issue in SC