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"Interior design firms"
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Meyer Davis : made to measure : architecture and interiors
\"Since forming their practice in 1999, Will Meyer and Gray Davis have designed some 200 private and public spaces that epitomize hip luxury style. Their interiors are as dazzling and dramatic as they are comfortable and welcoming--a carefully calibrated balancing act that has become their trademark and won them a loyal clientele. As furniture designer David Netto says in his foreword, \"Their style--while original--seems inevitable, and after you see a project by Meyer Davis you say to yourself, 'Why didn't I think of that?' \" Made to Measure tells the story of their ascent into the upper echelon of American design and shares their firm's philosophy and process. Illustrated with hundreds of stunning photographs, plans, and drawings, the book explores the symbiosis between their residential and commercial projects and shows how Meyer Davis has redefined modern luxury\"-- Provided by publblisher.
Book
IDBspRS: An Interior Design-Built Service Package Recommendation System Using Artificial Intelligence
Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners.
Journal Article
Saarinen houses
\"The iconic Finnish-American father-son architects Eliel and Eero Saarinen may be most famous for buildings such as the Helsinki Central Railway Station (Eliel), The Gateway Arch in St. Louis (Eero), and many other landmarks, but they also designed a number of remarkable houses. This book presents seventeen of the houses designed by the Saarinens, from Eliel's early twentieth-century Villa Pulkanranta, an eclectic mix of local Finnish design traditions and international influences; to Eliel's famous Art-Deco house at Cranbrook; to the architects' collaborative Koebel House, with its strong, horizontal lines; to the Loja Saarinen House, which Eero designed for his mother\"-- Provided by publisher.
Book
Empowering Digital Marketing with Interactive Virtual Reality (IVR) in Interior Design: Effects on Customer Satisfaction and Behaviour Intention
Interior design industries have evolved to adopt advanced digital and interactive virtual reality (IVR) technologies for promotion. Marketing using a platform with a virtual interior design feature is an approach that enables not only the building up of a positive image for an interior design firm but also allows customers to experience home design intuitively on the digital platform. This study researched the relationship between the three factors of aesthetics, ease of use, and information quality in digital marketing and consumer satisfaction. Data from 120 respondents were collected via the internet. The results generated from structural equation modelling indicated that the above factors positively influence customer satisfaction with a digital platform empowered with the IVR interior design. It was found that information quality has the most influence among the three factors. Despite numerous scholars having conducted in-depth research on digital marketing, existing research lacks a consumer perspective for examining what factors have the most significant impact on consumers. Moreover, relatively little work has been conducted to determine the customer’s perceptions towards the digital marketing approach using virtual interior design and its interactive features. A theoretical model for interactive virtual interior design features for digital marketing is thus proposed.
Journal Article
Analysis of pairings of colors and materials of furnishings in interior design with a data-driven framework
Color–material furnishing pairing is known as a “black-box” for interior designers. The overall atmosphere of a space can be changed by modifying furnishing combinations, e.g., to express modern or classic styles. Designers carefully choose pairings of colors and materials that fit their intended interior design styles based on experience and knowledge. However, no specific principles or rules have yet been established. Therefore, this study aims to derive a furnishing pairing principle based on a novel framework comprising object detection, color extraction, material recognition, and network analysis. We used the proposed framework to analyze large-scale interior design image data (N = 24194) collected from an online interior design platform. We also used the authenticity algorithm to analyze the relative influence of styles. By using the data-driven method from large-scale data in each of the eight interior styles, we derived authentic color, material, and furnishing combinations. Our study results revealed that images with high authenticity values in each style matched existing style descriptions. Additionally, the proposed framework allows interior style image retrieval based on a specific color, material, and furnishing combination. Our findings have implications for research on the development of style-aware furniture retrieval systems and automatic interior design generation methods.
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