1,300 research outputs found

    Modeling and predicting the popularity of online news based on temporal and content-related features

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    As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity

    Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models

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    Our research group wanted to take on the difficult task of predicting prices in a dynamic market. And short term rentals such as Airbnb listings seemed to be the perfect proving ground to do such a thing. Airbnb has revolutionized the travel industry by providing a platform for homeowners to rent out their properties to travelers. The pricing of Airbnb rentals is prone to high fluctuations, with prices changing frequently based on demand, seasonality, and other factors. Accurate prediction of Airbnb rental prices is crucial for hosts to optimize their revenue and for travelers to make informed booking decisions. In this project, we aim to predict the prices of Airbnb rentals using a machine learning modeling approach. Our project expands on earlier research in the area of analyzing Airbnb rental prices by taking a methodical machine learning approach as well as incorporating sentiment analysis into our feature engineering. We intend to gain a deeper understanding on periodic changes of Airbnb rental prices. The primary objective of this study is to construct an accurate machine learning model for predicting Airbnb rental prices specifically in Austin, Texas. Our project's secondary objective is to identify the key factors that drive Airbnb rental prices and to investigate how these factors vary across different locations and property types.Comment: 40 pages, 10 tables, 12 figure

    Predicting and explaining Airbnb prices in Lisbon : machine learning approach

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    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

    Towards Decrypting Attractiveness via Multi-Modality Cue

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    Decrypting the secret of beauty or attractiveness has been the pursuit of artists and philosophers for centuries. To date, the computational model for attractiveness estimation has been actively explored in the computer vision and multimedia community, yet with the focus mainly on facial features. In this article, we conduct a comprehensive study on female attractiveness conveyed by single/multiple modalities of cues, that is, face, dressing and/or voice; the aim is to discover how different modalities individually and collectively affect the human sense of beauty. To extensively investigate the problem, we collect the Multi-Modality Beauty (M2B) dataset, which is annotated with attractiveness levels converted from manual k-wise ratings and semantic attributes of different modalities. Inspired by the common consensus that middle-level attribute prediction can assist higher-level computer vision tasks, we manually labeled many attributes for each modality. Next, a tri-layer Dual-supervised Feature-Attribute-Task (DFAT) network is proposed to jointly learn the attribute model and attractiveness model of single/multiple modalities. To remedy possible loss of information caused by incomplete manual attributes, we also propose a novel Latent Dual-supervised Feature-Attribute-Task (LDFAT) network, where latent attributes are combined with manual attributes to contribute to the final attractiveness estimation. The extensive experimental evaluations on the collected M2B dataset well demonstrate the effectiveness of the proposed DFAT and LDFAT networks for female attractiveness prediction

    Performance modelling, analysis and prediction of Spark jobs in Hadoop cluster : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, School of Mathematical & Computational Sciences, Massey University, Auckland, New Zealand

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    Big Data frameworks have received tremendous attention from the industry and from academic research over the past decade. The advent of distributed computing frameworks such as Hadoop MapReduce and Spark are powerful frameworks that offer an efficient solution for analysing large-scale datasets running under the Hadoop cluster. Spark has been established as one of the most popular large-scale data processing engines because of its speed, low latency in-memory computation, and advanced analytics. Spark computational performance heavily depends on the selection of suitable parameters, and the configuration of these parameters is a challenging task. Although Spark has default parameters and can deploy applications without much effort, a significant drawback of default parameter selection is that it is not always the best for cluster performance. A major limitation for Spark performance prediction using existing models is that it requires either large input data or system configuration that is time-consuming. Therefore, an analytical model could be a better solution for performance prediction and for establishing appropriate job configurations. This thesis proposes two distinct parallelisation models for performance prediction: the 2D-Plate model and the Fully-Connected Node model. Both models were constructed based on serial boundaries for a certain arrangement of executors and size of the data. In order to evaluate the cluster performance, various HiBench workloads were used, and workload’s empirical data were fitted with the models for performance prediction analysis. The developed models were benchmarked with the existing models such as Amdahl’s, Gustafson, ERNEST, and machine learning. Our experimental results show that the two proposed models can quickly and accurately predict performance in terms of runtime, and they can outperform the accuracy of machine learning models when extrapolating predictions

    Recipe popularity prediction in Finnish social media by machine learning models

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    Abstract. In recent times, the internet has emerged as a primary source of cooking inspiration, eating experiences and food social gathering with a majority of individuals turning to online recipes, surpassing the usage of traditional cookbooks. However, there is a growing concern about the healthiness of online recipes. This thesis focuses on unraveling the determinants of online recipe popularity by analyzing a dataset comprising more than 5000 recipes from Valio, one of Finland’s leading corporations. Valio’s website serves as a representation of diverse cooking preferences among users in Finland. Through examination of recipe attributes such as nutritional content (energy, fat, salt, etc.), food preparation complexity (cooking time, number of steps, required ingredients, etc.), and user engagement (the number of comments, ratings, sentiment of comments, etc.), we aim to pinpoint the critical elements influencing the popularity of online recipes. Our predictive model-Logistic Regression (classification accuracy and F1 score are 0.93 and 0.9 respectively)- substantiates the existence of pertinent recipe characteristics that significantly influence their rates. The dataset we employ is notably influenced by user engagement features, particularly the number of received ratings and comments. In other words, recipes that garner more attention in terms of comments and ratings tend to have higher rates values (i.e., more popular). Additionally, our findings reveal that a substantial portion of Valio’s recipes falls within the medium health Food Standards Agency (FSA) score range, and intriguingly, recipes deemed less healthy tend to receive higher average ratings from users. This study advances our comprehension of the factors contributing to the popularity of online recipes, providing valuable insights into contemporary cooking preferences in Finland as well as guiding future dietary policy shift.Reseptin suosion ennustaminen suomalaisessa sosiaalisessa mediassa koneoppimismalleilla. Tiivistelmä. Internet on viime aikoina noussut ensisijaiseksi inspiraation lähteeksi ruoanlaitossa, ja suurin osa ihmisistä on siirtynyt käyttämään verkkoreseptejä perinteisten keittokirjojen sijaan. Huoli verkkoreseptien terveellisyydestä on kuitenkin kasvava. Tämä opinnäytetyö keskittyy verkkoreseptien suosioon vaikuttavien tekijöiden selvittämiseen analysoimalla yli 5000 reseptistä koostuvaa aineistoa Suomen johtavalta maitotuoteyritykseltä, Valiolta. Valion verkkosivujen reseptit edustavat monipuolisesti suomalaisten käyttäjien ruoanlaittotottumuksia. Tarkastelemalla reseptin ominaisuuksia, kuten ravintoarvoa (energia, rasva, suola, jne.), valmistuksen monimutkaisuutta (keittoaika, vaiheiden määrä, tarvittavat ainesosat, jne.) ja käyttäjien sitoutumista (kommenttien määrä, arviot, kommenttien mieliala, jne.), pyrimme paikantamaan kriittiset tekijät, jotka vaikuttavat verkkoreseptien suosioon. Ennustava mallimme — Logistic Regression (luokituksen tarkkuus 0,93 ja F1-pisteet 0,9 ) — osoitti merkitsevien reseptiominaisuuksien olemassaolon. Ne vaikuttivat merkittävästi reseptien suosioon. Käyttämiimme tietojoukkoihin vaikuttivat merkittävästi käyttäjien sitoutumisominaisuudet, erityisesti vastaanotettujen arvioiden ja kommenttien määrä. Toisin sanoen reseptit, jotka saivat enemmän huomiota kommenteissa ja arvioissa, olivat yleensä suositumpia. Lisäksi selvisi, että huomattava osa Valion resepteistä kuuluu keskitason terveyspisteiden alueelle (arvioituna FSA Scorella), ja mielenkiintoisesti, vähemmän terveellisiksi katsotut reseptit saavat käyttäjiltä yleensä korkeamman keskiarvon. Tämä tutkimus edistää ymmärrystämme verkkoreseptien suosioon vaikuttavista tekijöistä ja tarjoaa arvokasta näkemystä nykypäivän ruoanlaittotottumuksista Suomessa

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
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