Can Title Images Predict the Emotions and the Performance of Crowdfunding Projects?

Crowdfunding is a novel way to raise funds from individuals. However, taking Kickstarter for example, more than 60% of projects failed to reach the funding targets. Hence it is imperative to study how to improve the successfulness of the projects. From a design perspective, we intend to investigate that can the characteristics of title images of the projects on the search page of the crowdfunding website predict the performance of crowdfunding projects. We use objective standards to measure the aesthetic features of the title images. And we introduce emotions as important antecedents for the performance of a project. We used deep learning to extract the emotion metrics from the title images. Analysis results provide significant evidence that aesthetic attributes of images can predict emotion in images, and emotions, such as sadness and contentment, can predict the performance of crowdfunding projects. Our results provide both theoretical and practical values

Image Analysis to Assess the Impact of Photo Aesthetics on Online Consumer Click-through: An Empirical Study

Determinants of consumer’s shopping behavior are of long-term interest to researchers. Since product photos directly aid consumers’ understanding of products, retailers often put a lot of effort into polishing them. However, there is limited research on the impact of product photos on shopping behavior. This research takes advantage of image-processing techniques to study product photos’ impact. These techniques allow us to investigate a large set of photo characteristics simultaneously in an empirical study. To rule out possible confounding factors, we use a real company dataset from a social shopping Website, which has a simple interface allowing consumers to judge products mainly based on their photos. We employ two-stage nested logit model embedded with differences-in-differences approach and examine product photo characteristics from the aspects of color, composition, complexity, and model face. We found that consumers prefer to click product photos with a warmer color, a larger key object, appropriate complexity

Image visual similarity with deep learning: application to a fashion ecommerce company

Deep learning is a very trendy topic now, showing high accuracy in image based systems that can go from image segmentation to object detection and image retrieval. Because of this, multiple researchers and companies have been building and sharing work in the community, including pre-trained convolutional neural networks, available for public use. This work follows the trend and delivers an experimental study using deep learning for building a visually similar image retrieval application, comparing three different convolutional neural architectures for feature extraction and six distance indexes for similarity calculation in a real-world image retrieval problem, using real data from a fashion e-commerce platform from Morocco. After testing all the different combinations, we can conclude that for this dataset, Vgg19 combined with a correlation coefficient for similarity calculation is the tuple that best maximizes the similarity between a search image and its retrieved neighbors

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms