{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:47:26Z","timestamp":1760708846017,"version":"3.41.0"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2015,6,1]],"date-time":"2015-06-01T00:00:00Z","timestamp":1433116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"HK GRF","award":["415112"],"award-info":[{"award-number":["415112"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2015,6]]},"abstract":"<jats:p>Many Web services like Amazon, Epinions, and TripAdvisor provide historical product ratings so that users can evaluate the quality of products. Product ratings are important because they affect how well a product will be adopted by the market. The challenge is that we only have partial information on these ratings: each user assigns ratings to only a small subset of products. Under this partial information setting, we explore a number of fundamental questions. What is the minimum number of ratings a product needs so that one can make a reliable evaluation of its quality? How may users\u2019 misbehavior, such as cheating in product rating, affect the evaluation result? To answer these questions, we present a probabilistic model to capture various important factors (e.g., rating aggregation rules, rating behavior) that may influence the product quality assessment under the partial information setting. We derive the minimum number of ratings needed to produce a reliable indicator on the quality of a product. We extend our model to accommodate users\u2019 misbehavior in product rating. We derive the maximum fraction of misbehaving users that a rating aggregation rule can tolerate and the minimum number of ratings needed to compensate. We carry out experiments using both synthetic and real-world data (from Amazon and TripAdvisor). We not only validate our model but also show that the \u201caverage rating rule\u201d produces more reliable and robust product quality assessments than the \u201cmajority rating rule\u201d and the \u201cmedian rating rule\u201d in aggregating product ratings. Last, we perform experiments on two movie rating datasets (from Flixster and Netflix) to demonstrate how to apply our framework to improve the applications of recommender systems.<\/jats:p>","DOI":"10.1145\/2700386","type":"journal-article","created":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T15:13:25Z","timestamp":1433258005000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Mathematical Modeling and Analysis of Product Rating with Partial Information"],"prefix":"10.1145","volume":"9","author":[{"given":"Hong","family":"Xie","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, The People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John C. S.","family":"Lui","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, The People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,6]]},"reference":[{"volume-title":"Retrieved","year":"2012","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"volume-title":"Retrieved","year":"2013","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124368"},{"key":"e_1_2_1_5_1","unstructured":"Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer.   Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer."},{"volume-title":"Information Retrieval and Mining in Distributed Environments. Studies in Computational Intelligence","author":"Boratto Ludovico","key":"e_1_2_1_6_1"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/501158.501175"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.137"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972818.32"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/352871.352889"},{"key":"e_1_2_1_11_1","first-page":"361","article-title":"Generalization of a probability limit theorem of Cramer","volume":"54","author":"Feller William","year":"1943","journal-title":"Transactions of the American Mathematical Society"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/11735106_63"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718518"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/223904.223929"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1008992.1009124"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/956863.956922"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242759"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341560"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/988672.988726"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.77"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2109205.2109209"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:INRT.0000048492.50961.a6"},{"key":"e_1_2_1_26_1","unstructured":"Jir\u0161\u00ed Matous\u0161ek and Jan Vondr\u00e1k. 2001. The Probabilistic Method. Charles University.  Jir\u0161\u00ed Matous\u0161ek and Jan Vondr\u00e1k. 2001. The Probabilistic Method. Charles University."},{"volume-title":"Proceedings of the 21st National Conference on Artificial Intelligence. 1388--1393","author":"Mobasher Bamshad","key":"e_1_2_1_27_1"},{"volume-title":"Retrieved","year":"2009","key":"e_1_2_1_28_1"},{"volume-title":"Retrieve","year":"2015","key":"e_1_2_1_29_1"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/192844.192905"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245121"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/379437.379731"},{"volume-title":"Proceedings of the Web Mining for E-Commerce\u2014Challenges and Opportunities Workshop.","author":"Sarwar Badrul M.","key":"e_1_2_1_33_1"},{"volume-title":"Retrieved","year":"2004","author":"Times New York","key":"e_1_2_1_34_1"},{"volume-title":"Retrieved","year":"2009","author":"Journal Wall Street","key":"e_1_2_1_35_1"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/11899402_5"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1100.1232"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.22"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1151454.1151495"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2700386","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2700386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T05:07:44Z","timestamp":1750223264000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2700386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015,6]]}},"alternative-id":["10.1145\/2700386"],"URL":"https:\/\/doi.org\/10.1145\/2700386","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2015,6]]},"assertion":[{"value":"2013-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2014-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2015-06-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}