Art Therapy (AT) is an established practice that facilitates emotional processing and recovery through creative expression. Recently, Visual Art Recommender Systems (VA RecSys) have emerged to support AT, demonstrating their potential by personalizing therapeutic artwork recommendations. Nonetheless, current VA RecSys rely on visual stimuli for user modeling, limiting their ability to capture the full spectrum of emotional responses during preference elicitation. Previous studies have shown that music stimuli elicit unique affective reflections, presenting an opportunity for cross-domain recommendation (CDR) to enhance personalization in AT. Since CDR has not yet been explored in this context, we propose a family of CDR methods for AT based on music-driven preference elicitation. A large-scale study with 200 users demonstrates the efficacy of music-driven preference elicitation, outperforming the classic visual-only elicitation approach. Our source code, data, and models are available at https://github.com/ArtAICare/Affect-aware-CDR

We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transactional queries on Google SERPs, collected from 47 participants, and includes: (1)~HTML source files, with CSS and images; (2)~rendered SERP screenshots; (3)~eye movement data; (4)~mouse movement data; (5)~bounding boxes of direct display and organic advertisements; and (6)~scripts for further preprocessing the data. In this paper we provide an overview of the dataset and baseline experiments (classification tasks) that can inspire researchers about the different possibilities for future work.

Modern Search Engine Results Pages (SERPs) present complex layouts where multiple elements compete for visibility. Attention modelling is crucial for optimising web design and computational advertising, whereas attention metrics can inform ad placement and revenue strategies. We introduce AdSight, a method leveraging mouse cursor trajectories to quantify in a scalable and accurate manner user attention in multi-slot environments like SERPs. AdSight uses a novel Transformer-based sequence-to-sequence architecture where the encoder processes cursor trajectory embeddings, and the decoder incorporates slot-specific features, enabling robust attention prediction across various SERP layouts. We evaluate our approach on two Machine Learning tasks: (1) regression, to predict fixation times and counts; and (2) classification, to determine some slot types were noticed. Our findings demonstrate the model's ability to predict attention with unprecedented precision, offering actionable insights for researchers and practitioners.

Information retrieval (IR) relies on a general notion of relevance, which is used as the principal foundation for ranking and evaluation methods. However, IR does not account for more a nuanced affective experience. Here, we consider the emotional response decoded directly from the human brain as an alternative dimension of relevance. We report an experiment covering seven different scenarios in which we measure and predict how users emotionally respond to visual image contents by using functional near-infrared spectroscopy (fNIRS) neuroimaging on two commonly used affective dimensions: valence (negativity and positivity) and arousal (boredness and excitedness). Our results show that affective states can be successfully decoded using fNIRS, and utilized to complement the present notion of relevance in IR studies. For example, we achieved 0.39 Balanced accuracy and 0.61 AUC in 4-class classification of affective states (vs. 0.25 Balanced accuracy and 0.5 AUC of a random classifier). Likewise, we achieved 0.684 Precision@20 when retrieving high-arousal images. Our work opens new avenues for incorporating emotional states in IR evaluation, affective feedback, and information filtering.

Being able to describe any user interface (UI) screenshot in natural language can promote understanding of the main purpose of the UI, yet currently it cannot be accomplished with state-of-the-art captioning systems. We introduce XUI, a novel method inspired by the global precedence effect to create informative descriptions of UIs, starting with an overview and then providing fine-grained descriptions about the most salient elements. XUI builds upon computational models for topic classification, visual saliency prediction, and natural language generation (NLG). XUI provides descriptions with up to three different granularity levels that, together, describe what is in the interface and what the user can do with it. We found that XUI descriptions are highly readable, are perceived to accurately describe the UI, and score similarly to human-generated UI descriptions. XUI is available as open-source software.

This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology. We demonstrate how straightforward is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. Based on our results, we propose an adversarial method to mitigate user profiling techniques that make use of mouse cursor tracking, such as the recurrent neural net we analyze in this paper. We also release our data and a web browser extension that implements our adversarial method, so that others can benefit from this work in practice.

Searchers often make a choice in a matter of seconds on SERPs. As a result of a dynamic cognitive process, choice is ultimately reflected in motor movement and thus can be modeled by tracking the computer mouse. However, because not all movements have equal value, it is important to understand how do they and, critically, their sequence length impact model performance. We study three different SERP scenarios where searchers (1)~noticed an advertisement, (2)~abandoned the page, and (3)~became frustrated. We model these scenarios with recurrent neural nets and study the effect of mouse sequence padding and truncating to different lengths. We find that it is possible to predict the aforementioned tasks sometimes using just 2 seconds of movement. Ultimately, by efficiently recording the right amount of data, we can save valuable bandwidth and storage, respect the users' privacy, and increase the speed at which machine learning models can be trained and deployed. Considering the web scale, doing so will have a net benefit on our environment.

Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-through data is challenging because search engines must rely on other behavioral signals to understand the underlying search intent. We show that mouse cursor movements make a valuable, low-cost behavioral signal that can discriminate good and bad abandonment. We model mouse movements on SERPs using recurrent neural nets and explore several data representations that do not rely on expensive hand-crafted features and do not depend on a particular SERP structure. We also experiment with data resampling and augmentation techniques that we adopt for sequential data. Our results can help search providers to gauge user satisfaction for queries without clicks and ultimately contribute to a better understanding of search engine performance.

Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, which is a time-consuming approach that often requires domain expertise. We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks that can predict user attention to direct displays, such as SERP advertisements. Our models are trained over raw mouse cursor data and achieve competitive performance. We conclude that neural network models should be adopted for downstream tasks involving mouse cursor movements, since they can provide an invaluable implicit feedback signal for re-ranking and evaluation.

Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.

Predicting user engagement with direct displays (DD) is of paramount importance to commercial search engines, as well as to search performance evaluation. However, understanding within-content engagement on a web page is not a trivial task mainly because of two reasons: (1) engagement is subjective and different users may exhibit different behavioural patterns; (2) existing proxies of user engagement (e.g., clicks, dwell time) suffer from certain caveats, such as the well-known position bias, and are not as effective in discriminating between useful and non-useful components. In this paper, we conduct a crowdsourcing study and examine how users engage with a prominent web search engine component such as the knowledge module (KM) display. To this end, we collect and analyse more than 115k mouse cursor positions from 300 users, who perform a series of search tasks. Furthermore, we engineer a large number of meta-features which we use to predict different proxies of user engagement, including attention and usefulness. In our experiments, we demonstrate that our approach is able to predict more accurately different levels of user engagement and outperform existing baselines.

Training a high-quality gesture recognizer requires providing a large number of examples to enable good performance on unseen, future data. However, recruiting participants, data collection, and labeling, etc., necessary for achieving this goal are usually time consuming and expensive. Thus, it is important to investigate how to empower developers to quickly collect gesture samples for improving UI usage and user experience. In response to this need, we introduce Gestures à Go Go ( g 3), a web service plus an accompanying web application for bootstrapping stroke gesture samples based on the kinematic theory of rapid human movements. The user only has to provide a gesture example once, and g 3 will create a model of that gesture. Then, by introducing local and global perturbations to the model parameters, g 3 generates from tens to thousands of synthetic human-like samples. Through a comprehensive evaluation, we show that synthesized gestures perform equally similar to gestures generated by human users. Ultimately, this work informs our understanding of designing better user interfaces that are driven by gestures.

The increasing availability of large volumes of human-curated content is shifting web search towards a paradigm that introduces seamlessly more semantic information to search engine result pages. This trend has resulted in the design of a new element known as the knowledge module (KM) where certain facts about named entities, obtained from various knowledge bases, are shown to users. So far, little has been done to uncover the role that this module plays on user experience in web search and whether it is perceived by users as a useful aid for their search tasks. Our work is an early attempt to bridge this gap. To this end, we conducted a crowdsourcing study aimed at understanding the effect of the KM on users' search experience and its overall utility. In particular, our study is the first to provide insights about the noticeability and usefulness of the KM in web search, together with comprehensive analyses of usability and workload.

Processing data on any sort of user interaction is well known to be cumbersome and mostly time consuming. In order to assist researchers in easily inspecting fine-grained browsing data, current tools usually display user interactions as mouse cursor tracks, a video-like visualization scheme. However, to date, traditional online video inspection has not explored the full capabilities of hypermedia and interactive techniques. In response to this need, we have developed SMT2ϵ, a Web-based tracking system for analyzing browsing behavior using feature-rich hypervideo visualizations. We compare our system to related work in academia and the industry, showing that ours features unprecedented visualization capabilities. We also show that SMT2ϵ efficiently captures browsing data and is perceived by users to be both helpful and usable. A series of prediction experiments illustrate that raw cursor data are accessible and can be easily handled, providing evidence that the data can be used to construct and verify research hypotheses. Considering its limitations, it is our hope that SMT2ϵ will assist researchers, usability practitioners, and other professionals interested in understanding how users browse the Web.