We present a case study of productive flyby users (PFB users) on a recommendation website.These users exhibit counterintuitive behavior: they input a large amount of data during their first visit but never return.This phenomenon can have both positive and negative impacts on the system.On the positive side, their high productivity contributes a substantial amount of data.On the negative side, they may input inappropriate ratings that violate the assumptions of recommendation algorithms, potentially undermining system performance.To better understand the nature and causes of this behavior, we investigated their motivations, expectations, reasons for leaving, and the potential risks associated with their ratings using a mixed-methods approach.Specifically, we conducted interviews with 11 users, surveyed 41 users, and analyzed the impact of 1,000 PFB users on the performance of recommendation algorithms for regular users.Our findings revealed diverse motivations among PFB users.Some engaged with the system merely to pass the time, while others had unrealistic expectations of the recommender system.Regarding rating quality, 27% of surveyed users admitted to rating movies they had not seen, citing reasons such as browsing too quickly or attempting to manipulate the algorithm.Notably, users who reported leaving because they were "just killing time and forgot about the website" were the most likely to rate unseen movies.Overall, PFB users significantly influence recommendation algorithms and their performance for regular users.While some subgroups negatively affect prediction accuracy, others provide

Researchers worldwide have explored the behavioral nuances that emerge from interactions of individuals afflicted by mental health disorders (MHD) with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to a persuasive technology that is part of their everyday lives: web search engines (SE). Each day, users with MHD embark on information seeking journeys using popular SE, like Google or Bing. Every step of the search process for better or worse has the potential to influence a searcher’s mindset. In this work, we empirically investigate what subliminal stimulus SE present to these vulnerable individuals during their searches. For this, we use synthetic queries to produce associated query suggestions and search engine results pages. Then we infer the subliminal stimulus present in text from SE, i.e., query suggestions, snippets, and web resources. Findings from our empirical analysis reveal that the subliminal stimulus displayed by SE at different stages of the information seeking process differ between MHD searchers and our control group composed of “average” SE users. Outcomes from this work showcase open problems related to query suggestions, search engine result pages, and ranking that the information retrieval community needs to address so that SE can better support individuals with MHD.

Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children’s offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6–17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.

We present the preliminary exploration we conducted to identify traits that can influence children’s preferences in books. Findings offer insights for the design of recommender algorithms that would look beyond patterns inferred from traditional user-system interactions (e.g., ratings) for recommendation purposes, since when it comes to children such data is rarely, if at all, available.

We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation strategies for children, StoryTime can generate suggestions without historical data or adult guidance. We discuss the benefits of StoryTime as a starting point for further research exploring the cold start problem, incorporating historical data, and needs related to children as a complex audience to enhance the recommendation process.