GroupLink

Wei, Honghao; Hsieh, Cheng-Kang; Yang, Longqi; Estrin, Deborah

doi:10.1145/2818052.2874338

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…However, like other history-based recommendation [34,82], the proposed approach also suffers from the cold-start problem [89], i.e., unable to provide recommendation for newcomers who do not own any history yet. To accommodate cold-start problems, one can use a calibrated characterization for newcomers, e.g., incorporate such static attributes of the workers as occupation, interest for modeling.…”

Section: Objectivity Vs Fairnessmentioning

confidence: 99%

Context- and Fairness-Aware In-Process Crowdworker Recommendation

Wang

Yang

Wang

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to improve bug detection efficiency, i.e., detect more bugs with fewer workers. However, there are a couple of limitations in existing work. First, these studies mainly focus on one-time recommendations based on expertise matching at the beginning of a new task. Second, the recommendation results suffer from severe popularity bias, i.e., highly experienced workers are recommended in almost all the tasks, while less experienced workers rarely get recommended. This article argues the need for context- and fairness-aware in-process crowdworker recommendation in order to address these limitations. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating crowdtesting by recommending appropriate workers in a dynamic manner, so that the non-yielding windows could be shortened. Besides, motivated by the popularity bias in existing crowdworker recommendation approach, this study also aims at alleviating the unfairness in recommendations. Driven by these observations, this article proposes a context- and fairness-aware in-process crowdworker recommendation approach, iRec2.0, to detect more bugs earlier, shorten the non-yielding windows, and alleviate the unfairness in recommendations. It consists of three main components: (1) the modeling of dynamic testing context, (2) the learning-based ranking component, and (3) the multi-objective optimization-based re-ranking component. The evaluation is conducted on 636 crowdtesting tasks from one of the largest crowdtesting platforms, and results show the potential of iRec2.0 in improving the cost-effectiveness of crowdtesting by saving the cost, shortening the testing process, and alleviating the unfairness among workers. In detail, iRec2.0 could shorten the non-yielding window by a median of 50%–66% in different application scenarios, and consequently have potential of saving testing cost by a median of 8%–12%. Meanwhile, the recommendation frequency of the crowdworker drop from 34%–60% to 5%–26% under different scenarios, indicating its potential in alleviating the unfairness among crowdworkers.

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Section: Objectivity Vs Fairnessmentioning

confidence: 99%

Context- and Fairness-Aware In-Process Crowdworker Recommendation

Wang

Yang

Wang

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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“…dining out, drinks, movie, etc.) 6 [49,92,163,188,210,211] Domain Independent Recommendations 6 [64,[191][192][193][194][195] More than one application domain (not domain independent) 5 [33,39,59,90,109] Engineering Requirements 4 [63,65,146,178] News Recommendations 4 [39,41,153,166] Websites, Social Media Content, Microblogs 4 [110,112,168,184] University/learning Context 3 [11,109,216] Product Recommendations 2 [59,71] Artworks Sequence in Museums 2 [170,171] House Recommendations 1 [151] Book Recommendations 1 [54] Car Recommendations 1 [180] given the increasing mobility and ride-sharing among commuters, this could be an exciting research direction to explore GRS in which commuters could collectively choose which is the best route to take every time they share a ride based on their interest, such as scenic views, gas or time saving, arriving order, and others.…”

Section: The Application Domains Selected To Design Human Interfaces ...mentioning

confidence: 99%

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

Alvarado

Htun

Jin

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Therefore, we systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains; (2) devices chosen to implement the systems; (3) prototype fidelity; (4) strategies for profile transparency, justification, control, and diversity; (5) strategies for group formation and final group consensus; and, (6) evaluation methods applied in user studies during the design process. Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts.

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