{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:13:45Z","timestamp":1760148825734,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Games"],"abstract":"<jats:p>Nowadays, rating systems play a crucial role in the attraction of customers to different services. However, as it is difficult to detect a fake rating, fraudulent users can potentially unfairly impact the rating\u2019s aggregated score. This fraudulent behavior can negatively affect customers and businesses. To improve rating systems, in this paper, we take a novel mechanism-design approach to increase the cost of fake ratings while providing incentives for honest ratings. However, designing such a mechanism is a challenging task, as it is not possible to detect fake ratings since raters might rate a same service differently. Our proposed mechanism RewardRating is inspired by the stock market model in which users can invest in their ratings for services and receive a reward on the basis of future ratings. We leverage the fact that, if a service\u2019s rating is affected by a fake rating, then the aggregated rating is biased toward the direction of the fake rating. First, we formally model the problem and discuss budget-balanced and incentive-compatibility specifications. Then, we suggest a profit-sharing scheme to cover the rating system\u2019s requirements. Lastly, we analyze the performance of our proposed mechanism.<\/jats:p>","DOI":"10.3390\/g13040052","type":"journal-article","created":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T23:37:29Z","timestamp":1659310649000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RewardRating: A Mechanism Design Approach to Improve Rating Systems"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3015-2518","authenticated-orcid":false,"given":"Iman","family":"Vakilinia","sequence":"first","affiliation":[{"name":"School of Computing, University of North Florida, Jacksonville, FL 32224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peyman","family":"Faizian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Mahdi","family":"Khalili","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Delaware, Newark, DE 19716, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","unstructured":"Murphy, R. (2021, June 28). Local Consumer Review Survey. Available online: https:\/\/www.brightlocal.com\/research\/local-consumer-review-survey."},{"key":"ref_2","unstructured":"Streitfeld, D. (2021, June 28). Give Yourself 5 Stars? Online, It Might Cost You. Available online: https:\/\/www.nytimes.com\/2013\/09\/23\/technology\/give-yourself-4-stars-online-it-might-cost-you.html."},{"key":"ref_3","unstructured":"FTC (2021, June 28). FTC Brings First Case Challenging Fake Paid Reviews on an Independent Retail Website, Available online: https:\/\/www.ftc.gov\/news-events\/press-releases\/2019\/02\/ftc-brings-first-case-challenging-fake-paid-reviews-independent."},{"key":"ref_4","unstructured":"Amazon (2021, June 28). Anti-Manipulation Policy for Customer Reviews. Available online: https:\/\/www.amazon.com\/gp\/help\/customer\/display."},{"key":"ref_5","unstructured":"Google (2021, June 28). Prohibited and Restricted Content. Available online: https:\/\/support.google.com\/local-guides\/answer\/7400114?hl=en."},{"key":"ref_6","unstructured":"Yelp (2021, June 28). Content Guidelines. Available online: https:\/\/www.yelp.com\/guidelines."},{"key":"ref_7","unstructured":"Yelp (2021, June 28). Yelp\u2019s Recommendation Software Explained. Available online: https:\/\/blog.yelp.com\/2010\/03\/yelp-review-filter-explained."},{"key":"ref_8","unstructured":"Birchall, G. (2021, June 28). One in Three TripAdvisor Reviews Are Fake, with Venues Buying Glowing Reviews, Investigation Finds. Available online: https:\/\/www.foxnews.com\/tech\/one-in-three-tripadvisor-reviews-are-fake-with-venues-buying-glowing-reviews-investigation-finds."},{"key":"ref_9","unstructured":"Crockett, Z. (2021, June 28). 5-Star Phonies: Inside the Fake Amazon Review Complex. Available online: https:\/\/thehustle.co\/amazon-fake-reviews."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1016\/j.procs.2020.03.299","article-title":"Game Theoretical Defense Mechanism Against Reputation Based Sybil Attacks","volume":"167","author":"Kumar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","unstructured":"Levine, B.N., Shields, C., and Margolin, N.B. (2006). A Survey of Solutions to the Sybil Attack, University of Massachusetts Amherst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/2034575.2034593","article-title":"Sybil defenses via social networks: A tutorial and survey","volume":"42","author":"Yu","year":"2011","journal-title":"ACM SIGACT News"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17259","DOI":"10.1007\/s00521-020-04757-2","article-title":"Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining","volume":"32","author":"Hajek","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1002\/spy2.9","article-title":"Detecting opinion spams and fake news using text classification","volume":"1","author":"Ahmed","year":"2018","journal-title":"Secur. Priv."},{"key":"ref_15","first-page":"17259","article-title":"Fake online reviews: Literature review, synthesis, and directions for future research","volume":"32","author":"Wu","year":"2020","journal-title":"Decis. Support Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yao, Y., Viswanath, B., Cryan, J., Zheng, H., and Zhao, B.Y. (November, January 30). Automated crowdturfing attacks and defenses in online review systems. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA.","DOI":"10.1145\/3133956.3133990"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106348","DOI":"10.1016\/j.chb.2020.106348","article-title":"Spotting faked 5 stars ratings in E-Commerce using mouse dynamics","volume":"109","author":"Monaro","year":"2020","journal-title":"Comput. Hum. Behav."}],"container-title":["Games"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4336\/13\/4\/52\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:59:34Z","timestamp":1760140774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4336\/13\/4\/52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":17,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["g13040052"],"URL":"https:\/\/doi.org\/10.3390\/g13040052","relation":{},"ISSN":["2073-4336"],"issn-type":[{"type":"electronic","value":"2073-4336"}],"subject":[],"published":{"date-parts":[[2022,7,29]]}}}