{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:09:32Z","timestamp":1758636572475,"version":"3.41.0"},"reference-count":29,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2011,4,1]],"date-time":"2011-04-01T00:00:00Z","timestamp":1301616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2011,4]]},"abstract":"<jats:p>Electronic Word-of-Mouth (eWOM) is growing exponentially with the rapid development of electronic commerce. As a result, consumers are increasingly crowded by a huge amount of eWOM contents and therefore there is a need to automatically recommend eWOM contents that are helpful to them. Existing helpfulness assessment approaches that deterministically estimate the helpfulness of eWOM contents lack a generative formulation and are limited to the training set that has been voted by many readers. This article presents a rigorous probabilistic framework for inferring the \u201chelpfulness\u201d of eWOM contents which can build a \u201chelpfulness\u201d model from a low number of votes on eWOM contents. Furthermore, we introduce a measurement, \u201chelpfulness\u201d bias, as the benchmark for the \u201chelpfulness\u201d of eWOM documents. We also propose a model that exploits the graphical model and expectation maximization algorithm, under this probabilistic framework, to demonstrate the versatility of our framework. Our algorithm is compared experimentally to other existing helpfulness discovering algorithms and the experimental results show that our framework can effectively model the helpfulness of eWOM contents better than other approaches, and therefore indicate the capability of our framework to recommend helpful eWOMs to potential consumers.<\/jats:p>","DOI":"10.1145\/1961189.1961195","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms"],"prefix":"10.1145","volume":"2","author":[{"given":"Richong","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Ottawa, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Tran","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2011,5,6]]},"reference":[{"volume-title":"Neural Networks for Pattern Recognition 1 Ed","author":"Bishop C. 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Mayzlin D. and Chevalier J. 2003. The effect of word of mouth on sales: Online book reviews. NBER working paper 10148.","DOI":"10.3386\/w10148"},{"key":"e_1_2_1_15_1","unstructured":"Nielsen-Online H. S. 2008. 81 percent of online holiday shoppers read online customer reviews according to nielsen online. http:\/\/www.nielsen-online.com\/pr\/pr-081218.pdf  Nielsen-Online H. S. 2008. 81 percent of online holiday shoppers read online customer reviews according to nielsen online. http:\/\/www.nielsen-online.com\/pr\/pr-081218.pdf"},{"key":"e_1_2_1_16_1","unstructured":"Palmer A. 2009. Web shoppers trust customer reviews more than friends. http:\/\/www.adweek. com\/aw\/content_display\/news\/e3id519a27d3f06495a04bf682811fac165  Palmer A. 2009. 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