{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T10:07:29Z","timestamp":1782382049680,"version":"3.54.5"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T00:00:00Z","timestamp":1515542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"GRF","award":["14630815"],"award-info":[{"award-number":["14630815"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2018,6,30]]},"abstract":"<jats:p>\n            Reputation systems have become an indispensable component of modern E-commerce systems, as they help buyers make informed decisions in choosing trustworthy sellers. To attract buyers and increase the transaction volume, sellers need to earn reasonably high reputation scores. This process usually takes a substantial amount of time. To accelerate this process, sellers can provide price discounts to attract users, but the underlying difficulty is that sellers have no prior knowledge on buyers\u2019 preferences over price discounts. In this article, we develop an online algorithm to infer the optimal discount rate from data. We first formulate an optimization framework to select the optimal discount rate given buyers\u2019 discount preferences, which is a tradeoff between the\n            <jats:italic>short-term profit<\/jats:italic>\n            and the\n            <jats:italic>ramp-up time<\/jats:italic>\n            (for reputation). We then derive the closed-form optimal discount rate, which gives us key insights in applying a\n            <jats:italic>stochastic bandits framework<\/jats:italic>\n            to infer the optimal discount rate from the transaction data with regret upper bounds. We show that the computational complexity of evaluating the performance metrics is infeasibly high, and therefore, we develop efficient randomized algorithms with guaranteed performance to approximate them. Finally, we conduct experiments on a dataset crawled from eBay. Experimental results show that our framework can trade 60% of the short-term profit for reducing the ramp-up time by 40%. This reduction in the ramp-up time can increase the long-term profit of a seller by at least 20%.\n          <\/jats:p>","DOI":"10.1145\/3154417","type":"journal-article","created":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T16:51:38Z","timestamp":1515603098000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Enhancing Reputation via Price Discounts in E-Commerce Systems"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7210","authenticated-orcid":false,"given":"Hong","family":"Xie","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Republic of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard T. B.","family":"Ma","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Republic of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John C. S.","family":"Lui","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, The People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,1,10]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Alibaba. 1999. Online Shopping Website. Retrieved from http:\/\/www.alibaba.com\/.  Alibaba. 1999. Online Shopping Website. Retrieved from http:\/\/www.alibaba.com\/."},{"key":"e_1_2_1_2_1","unstructured":"Amazon. 1994. Online Shopping Website. Retrieved from http:\/\/www.amazon.com\/.  Amazon. 1994. Online Shopping Website. Retrieved from http:\/\/www.amazon.com\/."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013689704352"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/4132332"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000024"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/501158.501177"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.45"},{"key":"e_1_2_1_8_1","unstructured":"eBay. 1995. Homepage. Retrieved from http:\/\/www.ebay.com\/.  eBay. 1995. Homepage. Retrieved from http:\/\/www.ebay.com\/."},{"key":"e_1_2_1_9_1","unstructured":"eBay. 1995. eBay Classifies Sellers into Twelve Stars. Retrieved from http:\/\/pages.ebay.com\/help\/feedback\/scores-reputation.html.  eBay. 1995. eBay Classifies Sellers into Twelve Stars. Retrieved from http:\/\/pages.ebay.com\/help\/feedback\/scores-reputation.html."},{"key":"e_1_2_1_10_1","unstructured":"Fortune 500. 2015. Fortune Ranking. Retrieved from http:\/\/fortune.com\/fortune500\/.  Fortune 500. 2015. Fortune Ranking. Retrieved from http:\/\/fortune.com\/fortune500\/."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","volume-title":"Multi-armed Bandit Allocation Indices","author":"Gittins John","DOI":"10.1002\/9780470980033"},{"key":"e_1_2_1_12_1","series-title":"Series B","volume-title":"Bandit processes and dynamic allocation indices. Journal of the Royal Statistical Society","author":"Gittins John C.","year":"1979"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/988672.988727"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.141"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1592451.1592452"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1530-9134.2006.00103.x"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1756-2171.2006.tb00067.x"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/775152.775242"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.2307\/1060783"},{"key":"e_1_2_1_20_1","unstructured":"Walter Loeb. 2014. 10 Reasons Why Alibaba Blows Away Amazon and eBay. Retrieved from http:\/\/www.forbes.com\/sites\/walterloeb\/2014\/04\/11\/10-reasons-why-alibaba-is-a-worldwide-leader-in-e-commerce\/.  Walter Loeb. 2014. 10 Reasons Why Alibaba Blows Away Amazon and eBay. Retrieved from http:\/\/www.forbes.com\/sites\/walterloeb\/2014\/04\/11\/10-reasons-why-alibaba-is-a-worldwide-leader-in-e-commerce\/."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1050.0379"},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","volume-title":"Probability and Computing","author":"Mitzenmacher Michael","DOI":"10.1017\/CBO9780511813603"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/355112.355122"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1566374.1566423"},{"key":"e_1_2_1_25_1","volume-title":"Proc. of P2P.","author":"Singh Aameek","year":"2003"},{"key":"e_1_2_1_26_1","unstructured":"Taobao. 2003. Online Shopping Website. Retrieved from http:\/\/www.taobao.com\/.  Taobao. 2003. Online Shopping Website. 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