What Users See – Structures in Search Engine Results Pages

This paper investigates the composition of search engine results pages. We define what elements the most popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use quite different approaches to results pages composition and therefore, the user gets to see quite different results sets depending on the search engine and search query used. Organic results still play the major role in the results pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or Yahoo Answers). Since we used the .com interfaces of the search engines, results may not be valid for other country-specific interfaces

Predicting and explaining Airbnb prices in Lisbon : machine learning approach

Airbnb is an online platform that provides listing and arrangement for short-term local home renting services. Since its establishment in 2008, it has offered 7 million homes and rooms in more than 81,000 cities throughout 191 countries. Airbnb price prediction is a valuable and important task both for guests and hosts. Overall, for practical applications, these models can give a host an optimal price they should charge for their new listing. On the consumer side, this will help travellers determine whether the listing price they see is fair. Much research has been done in this field; however, the longitude and latitude of Airbnb listings are often disregarded. This project focuses on Airbnb price prediction using the most recent (Sep 2021) Airbnb data in Lisbon. Using Google Maps API, the original dataset was enriched with information on the number of ATMs, metro stations, bars and discos within a maximum radius of 1 km. Also, using the geodesic distance, the distance to the airport and the nearest attraction were computed for each listing. A Linear Regression and a Gradient Boosting algorithm were compared based on the original Airbnb dataset and the extended dataset to examine the impact of new features that have been identified. According to the results, all models perform better when the new features are included. The best results are achieved with the Gradient Boosting with the extended data, with an MAE of 0. 3102 and an adjusted R-squared of 0.4633.O Airbnb é uma plataforma online que fornece alojamento de curto prazo. Desde a sua criação em 2008, já ofereceu 7 milhões de residências e quartos em mais de 81.000 cidades, em 191 países. A previsão de preços do Aibnb é uma tarefa valiosa tanto para hóspedes como para anfitriões. No geral, estes modelos de previsão podem oferecer ao anfitrião o preço ideal que deve ser cobrado pelo alojamento. Do lado do consumidor, ajudará os hóspedes a determinar se o preço do anúncio é justo. Muitos estudos já abordaram este tema, no entanto, a longitude e a latitude são frequentemente desconsideradas. Este projeto foca-se na previsão de preços do Airbnb em Lisboa usando os dados mais recentes (setembro de 2021). Usando a API do Google Maps, o conjunto de dados original foi ampliado adicionando colunas com o número de ATMs, estações de metro, bares e discotecas num raio máximo de 1 km. Além disso, usando a distância geodésica, a distância até o aeroporto e até à atração mais próxima foram calculadas. Os resultados de uma regressão linear e de um Gradient Boosting, com base no conjunto de dados original do Airbnb e no conjunto de dados alargado são comparados para examinar o impacto das novas variáveis. De acordo com os resultados, todos os modelos apresentam melhor desempenho quando as novas variáveis são incluídas. Os melhores resultados são obtidos com o Gradient Boosting com os dados alargados, com um MAE 0,3102 e um adjusted R-squared de 0,4633

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

Next-Generation Media: The Global Shift

For over a decade the Aspen Institute Communications and Society Program has convened its CEO-level Forum on Communications and Society (FOCAS) to address specific issues relating to the impact of communications media on societal institutions and values. These small, invitation-only roundtables have addressed educational, democratic, and international issues with the aim of making recommendations to policy-makers, businesses and other institutions to improve our society through policies and actions in the information and communications sectors.In the summer of 2006 the forum took a different turn. It is clear there is a revolution affecting every media business, every consumer or user of media, and every institution affected by media. In a word, everyone. FOCAS sought to define the paradigm changes underway in the media, and to identify some of the significant repercussions of those changes on society."Next Generation Media" was a three-day meeting among leaders from new media (e.g., Google, craigslist, and Second Life) and mainstream media (e.g., The New York Times and Time), from business, government, academia and the non-profit sector, all seeking a broad picture of where the digital revolution is taking us.This report of the meeting, concisely and deftly written by Richard Adler, a longtime consultant in the field, weaves insights and anecdotes from the roundtable into a coherent document supplemented with his own research and data to form an accessible, coherent treatment of this very topical subject.The specific goals of the 2006 forum were to examine the profound changes ahead for the media industries, advertisers, consumers and users in the new attention economy; to understand how the development and delivery of content are creating new business models for commercial and non-commercial media; and to assess the impact of these developments on global relations, citizenship and leadership.The report thus examines the growth of the Internet and its effect on a rapidly changing topic: the impact of new media on politics, business, society, culture, and governments the world over. The report also sheds light on how traditional media will need to adapt to face the competition of the next generation media.Beginning, as the Forum did, with data from Jeff Cole's Center for the Digital Future at the University of Southern California, Adler documents the increasing popularity of the Internet for information, entertainment and communication. Users are increasingly generating and contributing content to the web and connecting to social networks. They are posting comments, uploading pictures, sharing videos, blogging and vlogging, chatting through instant messages or voice over Internet (VoIP), or emailing friends, business colleagues, neighbors and even strangers. As Cole observes, "Traditional media informed people but didn't empower them." New media do.The report describes three of the Internet's most successful ventures -- Wikipedia, Second Life, and craigslist. Wikipedia is a prime example of how an Internet platform allows its users to generate content and consume it. As a result of "wiki" software technology anyone can contribute or edit existing information free of cost. Second Life, a virtual world, sells virtual real estate where subscribers, in avatar form, can conduct conversations, go to lectures, even create a business. Craigslist, a predominantly free online classified site with listings in every major city in the United States, has become so popular that it is posing a significant threat to newspapers as it competes with their classified ad revenues.As a result of these and other new media phenomena, not the least being Google and Yahoo, print publications are wrestling with new business models that could entail fundamentally restructuring the way they operate. For instance, reporters are now expected to report a story on multiple media platforms and discuss them online with readers. Newspaper publisher Gannett is exploring the incorporation of usergenerated news or "citizen-journalism" into its news pages.In an era of abundant choices marketers have an even greater challenge to figure out how best to appeal to consumers. The report explores how marketers, e.g., of Hollywood movies or pomegranate juice, are moving from traditional or mainstream media to viral and other marketing techniques.For much of the world, the mobile phone rather than the computer is the most important communications device. Users depend on their phones to send and receive messages, pictures, and download information rather than just talk. In developing countries mobile phones are having an exceptional impact, penetrating regions which are not being serviced by land lines. Thus we are seeing new uses daily for this increased connectivity, from reporting election results in emerging democracies to opposing authoritarian governments in order to bring about new democracies.Meanwhile, the report discusses the need for the United States to develop a new form of public diplomacy rather than the traditional top-down approach to communicating to foreign citizens. This topic has been a recurring theme at FOCAS conferences the past few years, this year calling for more citizen diplomacy -- that is, more person-toperson contact across borders through uses of the new media. Indeed, Peter Hirshberg suggested that American leaders should listen more to the outside world to effectively manage what he called "Brand America."Finally, after acknowledging the detrimental effects that new technologies can bring about, the report discusses what role those technologies could play in expanding freedom and opportunity for the next generation. As a conclusion, FOCAS co-chair Marc Nathanson proposed adding a ninth goal to the United Nations Millennium Goals, namely, "to provide access to appropriate new technologies.

ReQwip : business plan and go-to-market strategy

textThe nature of this Report is to outline the proposed business opportunity for reQwip -- an online marketplace for buying, selling and renting sports equipment -- and the go-to-market strategy for this young startup. reQwip is an Austin, Texas-based technology company founded by students and alumni of The University of Texas at Austin for the purpuse of creating a mobile, peer-to-peer (P2P) marketplace for buying, selling and renting new and used sports equipment. ReQwip is launching its minimum viable product (MVP) in Spring 2014. The MVP is a liquid marketplace focused specifically on buying and selling new and used cycling and triathlon gear in Austin,TX and greater Central Texas. This MVP is our gateway into a sporting goods industry worth $150 billion in sales worldwide and$54 billion in the United States, of which $1-3 billion is used gear sales in the U.S.AdvertisingBusiness Administratio

A Frontline Decision Support System for Georgia Career Centers

The Workforce Investment Act (WIA) of 1998 emphasizes the integration and coordination of employment services. Central to achieving this aim is the federal requirement that local areas receiving WIA funding must establish one-stop centers, where providers of various employment services within a local labor market are assembled in one location. A major challenge facing staff in these centers is the expected large volume of customers resulting from relaxed program eligibility rules. Nonetheless, resources for assessment and counseling are limited. To help frontline staff in one-stop centers quickly assess customer needs and properly target services, the U.S. Department of Labor has funded development of a Frontline Decision Support System (FDSS). The FDSS is being pilot tested in the state of Georgia where one-stop centers are called Georgia Career Centers. Technical assistance on the project is being provided by the W.E. Upjohn Institute for Employment Research. FDSS is comprised of two main parts: 1) the systematic job search module, and 2) the service referral module. The systematic job search module is a means to undertake a structured search of vacancy listings. The module provides information about a customer's prospects for returning to a job like their prior one, provides a realistic assessment of likely reemployment earnings, identifies occupations related to the prior one, and screens job vacancy listings by region, occupation, and earnings requirements. The service referral module identifies the sequence of activities that most often lead to successful employment for clients with similar background characteristics. This paper documents the strategy and tools implemented to pilot test FDSS within the internet-based Georgia Workforce System. Pilot field operations in Georgia began in the Athens and Cobb-Cherokee Career Centers in July, 2002.Frontline, decision. Support, FDSS, Georgia, Eberts, O'Leary, Upjohn

Relatedness, National Boarders, Perceptions of Firms and the Value of Their innovations

The main goal of this dissertation is to better understand how external corporate stakeholder perceptions of relatedness affect important outcomes for companies. In pursuit of this goal, I apply the lens of category studies. Categories not only help audiences to distinguish between members of different categories, they also convey patterns of relatedness. In turn, this may have implications for understanding how audiences search, what they attend to, and how the members are ultimately valued. In the first chapter, I apply incites from social psychology to show how the nationality of audience members affects the way that they cognitively group objects into similar categories. I find that the geographic location of stock market analysts affect the degree to which they will revise their earnings estimates for a given company in the wake of an earnings miss by another firm in the same industry. Foreign analysts revise their earnings estimates downward more so than do local analysts, suggesting that foreign analysts ascribe the earnings miss more broadly and tend to lump companies located in the same country into larger groups than do local analysts. In the second chapter, I demonstrate that the structure of inter-category relationships can have consequential effects for the members of a focal category. Leveraging an experimental-like design, I study the outcomes of nanotechnology patents and the pattern of forward citations across multiple patent jurisdictions. I find that members of technology categories with many close category \u27neighbors\u27 are more broadly cited than members of categories with few category \u27neighbors.’ My findings highlight how category embeddedness and category system structure affect the outcomes of category members as well as the role that classification plays in the valuation of innovation. In the third chapter, I propose a novel and dynamic measure of corporate similarity that is constructed from the two-mode analyst and company coverage network. The approach creates a fine-grained continuous measure of company similarity that can be used as an alternative or supplement to existing static industry classification systems. I demonstrate the value of this new measure in the context of predicting financial market responses to merger and acquisition deals

Airbnb Valuation: A Machine Learning Approach

This thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental success on the peer-to-peer property sharing website Airbnb.com. Geographic factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values