| 1. |
- Alklind Taylor, Anna-Sofia, 1975-, et al.
(författare)
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Guardian Angel : Using Lighting Drones to Improve Traffic Safety, Sense of Security, and Comfort for Cyclists
- 2023
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Ingår i: Lecture Notes in Computer Science. - Cham : Springer. - 9783031480461 - 9783031480478 ; , s. 209-223
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Konferensbidrag (refereegranskat)abstract
- Active mobility, such as biking, faces a common challenge in Swedish municipalities due to the lack of adequate lighting during the dark winter months. Insufficient lighting infrastructure hinders individuals from choosing bicycles, despite the presence of well-maintained bike paths and a willingness to cycle. To address this issue, a project has been undertaken in the Swedish municipality of Skara for an alternative lighting solution using drones. A series of tests have been conducted based on drone prototypes developed for the selected bike paths. Participants were invited to cycle in darkness illuminated by drone lighting and share their mobility preferences and perception. This paper summarizes the users’ perception of drone lighting as an alternative to fixed lighting on bike paths, with a special focus on the impact on travel habits and the perceived sense of security and comfort. Most participants were regular cyclists who cited bad weather, time, and darkness as significant factors that deterred them from using bicycles more frequently, reducing their sense of security. With drone lighting, the participants appreciated the illumination’s moonlight-like quality and its ability to enhance their sense of security by illuminating the surroundings. On the technology side, they gave feedback on reducing the drone’s sound and addressing lighting stability issues. In summary, the test results showcase the potential of drone lighting as a viable alternative to traditional fixed lighting infrastructure, offering improved traffic safety, sense of security, and comfort. The results show the feasibility and effectiveness of this innovative approach, supporting transformation towards active and sustainable mobility, particularly in regions facing lighting challenges.
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| 2. |
- Anelli, V. W., et al.
(författare)
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RecSys 2021 challenge workshop : Fairness-aware engagement prediction at scale on Twiter's Home Timeline
- 2021
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Ingår i: RecSys 2021 - 15th ACM Conference on Recommender Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450384582 ; , s. 819-824
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Konferensbidrag (refereegranskat)abstract
- The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year's challenge brings the problem even closer to Twitter's real recommender systems by introducing latency constraints. We also increases the data size to encourage novel methods. Also, the data density is increased in terms of the graph where users are considered to be nodes and interactions as edges. The goal is twofold: to predict the probability of different engagement types of a target user for a set of Tweets based on heterogeneous input data while providing fair recommendations. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1-3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.
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| 3. |
- Bauer, Christine, et al.
(författare)
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Conformity Behavior in Group Playlist Creation
- 2020
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Konferensbidrag (refereegranskat)abstract
- A strong research record on conformity has evidenced that individuals tend to conform with a group’s majority opinion. In contrast to existing literature that investigates conformity to a majority group opinion against an objectively correct answer, the originality of our study lies in that we investigate conformity in a subjective context. The emphasis of our analysis lies on the concept of “switching direction” in favor or against an item. We present first results from an online experiment where groups of five had to create a music playlist. A song was added to the playlist with an unanimous positive decision only. After seeing the other group members’ ratings, participants had the opportunity to revise their own response. Our results suggest different conformity behaviors for originally favored compared to disliked songs. For favored songs, one negative judgement by another group member was sufficient to induce participants to downvote the song. For originally disliked songs, in contrast, a majority of positive judgements was needed to induce participants to switch their vote.
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| 4. |
- Bauer, C., et al.
(författare)
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The Effect of Ingroup Identification on Conformity Behavior in Group Decision-Making : The Flipping Direction Matters
- 2023
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Ingår i: <em>Proceedings of the Annual Hawaii International Conference on System Sciences</em>. - : IEEE Computer Society. - 9780998133164 ; , s. 2242-2251
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Konferensbidrag (refereegranskat)abstract
- Various social influences affect group decision-making processes. For instance, individuals may adapt their behavior to fit in with the group's majority opinion. Furthermore, ingroup favoritism may lead individuals to favor the ideas of ingroup members rather than the outgroup. So far, little is explored on how these phenomena of social conformity and ingroup favoritism manifest in group decision-making processes when a group has to decide in favor or against an item. We address such a scenario where the 'flipping direction' of conformity (in favor or against an item) matters. Specifically, we explore whether and how the ingroup favoritism manifests differently in terms of conformity behavior depending on the 'flipping direction'. The results show that group inclusiveness does not play a role in the general tendency to conform. However, when it comes to a negative flipping direction, a higher feeling of group inclusiveness seems to play a role; yet, for individualist cultures only.
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| 5. |
- Belli, Luca, et al.
(författare)
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The 2021 RecSys Challenge Dataset : Fairness is Not Optional
- 2021
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Ingår i: Proceedings of the Recommender Systems Challenge 2021. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450386937 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- After the success the RecSys 2020 Challenge, we are describing anovel and bigger dataset that was released in conjunction with theACM RecSys Challenge 2021. This year’s dataset is not only bigger(~1B data points, a 5 fold increase), but for the first time it take intoconsideration fairness aspects of the challenge. Unlike many staticdatsets, a lot of effort went into making sure that the dataset wassynced with the Twitter platform: if a user deleted their content,the same content would be promptly removed from the dataset too.In this paper, we introduce the dataset and challenge, highlightingsome of the issues that arise when creating recommender systemsat Twitter scale.
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| 6. |
- Eriksson, Marcus, et al.
(författare)
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Towards a User Experience Framework for Business Intelligence
- 2021
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Ingår i: Journal of Computer Information Systems. - : Taylor & Francis. - 0887-4417 .- 2380-2057. ; 61:5, s. 428-437
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Tidskriftsartikel (refereegranskat)abstract
- Business intelligence (BI) systems are software applications that are used to gather and process data and to deliver the processed data in understandable way to the end users. With a younger generation of users moving into key positions in organizations and enterprises higher user experience (UX) demands are placed on BI systems interfaces. Companies developing BI systems lack standardized routines for implementing UX in their BI solutions. The purpose of this study was to develop a theoretical framework based on existing research and combine it with empirical data gathered from professionals in BI systems industry in Sweden with the intention of proposing a UX framework applicable to BI systems development. The study resulted in a framework being developed using iterative build-evaluate iterations. The framework is a scalable UX framework for BI systems interfaces covering areas from planning and strategizing to implementation, maintenance, and evaluation.
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| 7. |
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| 8. |
- Ferwerda, Bruce, 1986-, et al.
(författare)
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Exploring Online Music Listening Behaviors of Musically Sophisticated Users
- 2019
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Ingår i: ACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. - New York, NY, USA : ACM. - 9781450367110 ; , s. 33-37
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Konferensbidrag (refereegranskat)abstract
- Due to the rise of available online music, a lot of music consumption is moving from traditional offline media to online sources. Online music sources offer almost an unlimited music collection to its users. Hence, how music is consumed by users (e.g., experts) may differ from traditional offline sources. In this work we explored how musically sophisticated users (i.e. experts) consume online music in terms of diversity. To analyze this, we gathered data from two different sources: Last.fm and Spotify. As expertise is defined by the ubiquitousness of experiences, we calculated different diversity measurements to explore how ubiquitous (in terms of diversity) the listening behaviors of users are. We found that different musical sophistication levels correspond to applying diversity related to specific kind of musical characteristics (i.e., artist or genre). Our results can provide knowledge on how systems should be designed to provide better support to expert users.
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| 9. |
- Ferwerda, Bruce, 1986-, et al.
(författare)
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Exploring the Prediction of Personality Traits from Drug Consumption Profiles
- 2020
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Ingår i: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450379502 ; , s. 2-5
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Konferensbidrag (refereegranskat)abstract
- The number of people that have been in touch with drugs is continuously increasing. Excessive intake of drugs becomes problematic when it turns into disorderly behaviors, such as addictions. In order to treat these disorderly behaviors, treatment plans often adhere to a one-size-fits-all approach with fixed and standardized steps. However, for effective treatment of disorderly behaviors it has been acknowledged that personalized treatment programs are necessary. The personality of people has been argued to be a factor that plays an important role in setting up effective treatment plans. In this work we explored the predictability of people’s personality traits based on their drug consumption profile. Based on self-reported consumption frequencies of "abusable psychoactive drugs," we found among 1878 respondents that drug consumption profiles can be used to predict people’s personality traits. The prediction of personality traits can be used to circumvent intruding questionnaires and to implicitly create personalized treatment programs.
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| 10. |
- Ferwerda, Bruce, 1986-, et al.
(författare)
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I Don't Care How Popular You Are! Investigating Popularity Bias in Music Recommendations from a User's Perspective
- 2023
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Ingår i: CHIIR ’23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. - New York, NY, USA : Association for Computing Machinery (ACM). - 9798400700354 ; , s. 357-361
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Konferensbidrag (refereegranskat)abstract
- Recommender systems are designed to help us navigate through an abundance of online content. Collaborative filtering (CF) approaches are commonly used to leverage behaviors of others with a similar taste to make predictions for the target user. However, CF is prone to introduce or amplify popularity bias in which popular (often consumed or highly ranked) items are prioritized over less popular items. Many computational metrics of popularity biases — and resulting algorithmic (un)fairness — have been presented. However, it is largely unclear whether these metrics reflect human perception of bias and fairness. We conducted a user study with 170 participants to explore how users perceive recommendation lists created by algorithms with different degrees of popularity bias. Our results show — surprisingly — that popularity biases in recommendation lists are barely observed by users, even when corresponding bias/fairness metrics clearly indicate them.
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