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Many decision-making scenarios, e.g., public policy, healthcare, business, and disaster response, require accommodating the preferences of multiple stakeholders. We offer the first formal treatment of reasoning with multi-stakeholder qualitative preferences in a setting where stakeholders express their preferences in a qualitative preference language, e.g., CP-net, CI-net, TCP-net, CP-Theory. We introduce a query language for expressing queries against such preferences over sets of outcomes that satisfy specified criteria, e.g., ψ1PAψ2 (read loosely as the set of outcomes satisfying ψ1 that are preferred over outcomes satisfying ψ2 by a set of stakeholders A). Motivated by practical application scenarios, we introduce and analyze several alternative semantics for such queries, and examine their interrelationships. We provide a provably correct algorithm for answering multi-stakeholder qualitative preference queries using model checking in alternation-free μ-calculus. We present experimental results that demonstrate the feasibility of our approach.
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Stakeholder Preference Extraction From Scenarios
Companies use personalization to tailor user experiences. Personalization appears in search engines and online stores, which include salutations and statistically learned correlations over search-, browsing- and purchase-histories. However, users have a wider variety of substantive, domain-specific preferences that affect their choices when they use directory services, and these have largely been overlooked or ignored. The contributions of this paper include: (1) a grounded theory describing how stakeholder preferences are expressed in text scenarios; (2) an app feature survey to assess whether elicited preferences represent missing requirements in existing systems; (3) an evaluation of three classifiers to label preference words in scenarios; and (4) a linker to build preference phrases by linking labeled preference words to each other based on word position. In this study, the authors analyzed 217 elicited directory service scenarios across 12 domain categories to yield a total of 7,661 stakeholder preferences labels. The app survey yielded 43 stakeholder preferences that were missed on average 49.7% by 15 directory service websites studied. The BERT-based transformer showed the best average overall 81.1% precision, 84.4% recall and 82.6% F1-score when tested on unseen domains. Finally, the preference linker correctly links preference phrases with 90.1% accuracy. Given these results, we believe directory service developers can use this approach to automatically identify user preferences to improve service designs.
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- PAR ID:
- 10561329
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Software Engineering
- Volume:
- 50
- Issue:
- 1
- ISSN:
- 0098-5589
- Page Range / eLocation ID:
- 69 to 84
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TSE.2023.3333265
