Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences

Umemoto, Kazutoshi; Milo, Tova

doi:10.1109/icde48307.2020.00022

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…The term "Explainable Recommendation" was first defined by Zhang et al [341]. As an important sub-field of AI and machine learning research and due to the fact that recommendation naturally involves humans in the loop, the recommender system community has been leading the research on Explainable AI ever since, which triggers a broader scope of explainability research in other AI and machine learning sub-fields [71,340], such as explainability in scientific research [181], computer vision [297], natural language processing [40,106,172,217,229], graph neural networks [265,299], database [112,291], healthcare systems [121,228,350], online education [9,20,216,264,277], psychological studies [271] and cyber-physical systems [10,12,134,135,241].…”

Section: Overview Of Explainable Recommendationmentioning

confidence: 99%

A Survey on Trustworthy Recommender Systems

Ge¹,

Liu²,

Fu³

et al. 2022

Preprint

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Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired counter-effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy and responsible recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy and responsible recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.

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Section: Overview Of Explainable Recommendationmentioning

confidence: 99%

A Survey on Trustworthy Recommender Systems

Ge¹,

Liu²,

Fu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Recommender Systems, broadly defined as systems that aim to support users in decision making by suggesting and offering relevant content, play an integral role in the rapid rise of HR Tech. Their applications range from assisting the talent acquisition process through matching [10], analyzing resumes or other user representations for candidate screening [22] and automated assessment [14,16], to broader tasks such as recommendations for upskilling [21].…”

Section: Description 1motivationmentioning

confidence: 99%

Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

Bogers

Graus²,

Kaya

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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“…The difficulty of a task 𝑡 is denoted by 𝜃 𝑡 . We assume that worker skills and task difficulty are uni-dimensional and that the skills improve monotonically: the skill level remains the same or increases as a worker completes more tasks [43,54]. 𝜃 0 𝑤 (the skill of a worker 𝑤 at iteration 0) and 𝜃 𝑡 are pre-computed (Section 5.1 under Exp.…”

Section: Formalismmentioning

confidence: 99%

“…the skill level remains the same or increases as time passes [43,54]. A worker's skill is updated as follows:…”

Section: Manuscript Submitted To Acmmentioning

confidence: 99%

See 1 more Smart Citation

Task Assignment Strategies for Crowd Worker Ability Improvement

Matsubara

Borromeo

Amer-Yahia

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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Workers are the most important resource in crowdsourcing. However, only investing in worker-centric needs, such as skill improvement, often conflicts with short-term platform-centric needs, such as task throughput. This paper studies learning strategies in task assignment in crowdsourcing and their impact on platform-centric needs. We formalize learning potential of individual tasks and collaborative tasks, and devise an iterative task assignment and completion approach that implements strategies grounded in learning theories.We conduct experiments to compare several learning strategies in terms of skill improvement, and in terms of task throughput and contribution quality. We discuss how our findings open new research directions in learning and collaboration.CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.

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Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences

Cited by 8 publications

References 30 publications

A Survey on Trustworthy Recommender Systems

A Survey on Trustworthy Recommender Systems

Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

Task Assignment Strategies for Crowd Worker Ability Improvement

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