SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

User judgements of the online world: factors influencing website appeal and user decision-making.

Websites are an integral part of everyday life but we rarely think about how their visual appeal shapes our responses to them. To understand this relationship, research has outlined a number of visual characteristics that may determine appeal. However, previous studies have often used small stimulus sets or made experimental assumptions about critical website characteristics without careful control, making findings difficult to interpret and generalise. Experiment 1 addressed this through creating a corpus of 480 website stimuli containing normative ratings of key characteristics responsible for website appeal. Subsequent studies employed this corpus, providing stimuli that were well controlled but still represented the wider domain. Experiment 2 examined the timescale of appeal judgements and the impact of verbal brand framing messages on these judgements. As expected, participants made rapid, reliable, judgements even when given only 500ms. However, exposure to positive brand framing had a negative effect on appeal ratings. A possible explanation is discussed in terms of brand placement prominence on consumer attitudes. In Experiment 3 participants evaluated the appeal of embedded website advertising in order to examine the impact of visual framing on appeal judgements. Advertisements were deemed more appealing when they appeared on appealing websites, although brand familiarity had a mediating role. Eye movements revealed a complex relationship between website and advertisement appeal and familiarity in determining where participants attended. In Experiment 4, website appeal judgements were compared between typical participants and participants with autism in order to examine the role of individual differences. Interestingly, despite careful manipulations few differences emerged. However, eye tracking data revealed ASD participants attended to detailed content more than their typical counterparts. The implications of this work are discussed and a revision to the model of aesthetic judgement (Leder et al., 2004) is proposed in order to account for the current findings. An information-processing model of website evaluations is presented which outlines the processes involved from making initial judgements of appeal through to later, long-term evaluations of a website

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

Effects of cultural cognitive styles on users evaluation of website complexity

The internationalization of websites requires compelling navigation experience for users from diverse cultures. This research investigates the effects of cultural cognitive styles on user perception of website complexity and the subsequent influence on user satisfaction towards the website. More specifically, the website complexity is examined along three dimensions: component, coordinative, and dynamic. Laboratory experiments involving participants from China and United States were used to test the hypotheses. The results showed that the effect of objective complexity on perceived complexity is contingent on cultural cognitive styles. People with holistic and analytic cultural cognitive styles display different perceptions of website complexity. This study extends website complexity literature to the cross-cultural context. It also suggests pragmatic strategies for website design practitioners to improve website design in order to attract international audiences

Effects of Cultural Cognitive Styles on Users\u27 Evaluation of Website Complexity

The internationalization of websites requires compelling navigation experience for users from diverse cultures. This research investigates the effects of cultural cognitive styles on user perception of website complexity and the subsequent influence on user satisfaction towards the website. More specifically, the website complexity is examined along three dimensions: component, coordinative, and dynamic. Laboratory experiments involving participants from China and United States were used to test the hypotheses. The results showed that the effect of objective complexity on perceived complexity is contingent on cultural cognitive styles. People with holistic and analytic cultural cognitive styles display different perceptions of website complexity. This study extends website complexity literature to the cross-cultural context. It also suggests pragmatic strategies for website design practitioners to improve website design in order to attract international audiences

Tablet Magazines and the Affects on the Magazine Industry

The purpose of this study is to determine the affects tablet magazines have on the magazine industry. It includes research on past and present circulation trends for print and digital magazine consumption. Interviews of industry professionals were conducted, giving their insight into the development and consumer attitudes toward iPad tablet magazines. An online survey questioning subscription, functionality and general views of both tablet and print magazines was distributed as well as an in person case study that allowed users to physically navigate, observe and compare an iPad magazine and its print counterpart. The results of this study can be used to predict the impact of a recent technological introduction—the iPad magazine and its affects on the current and future magazine industry