{"type":"ScholarlyArticle","title":"Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations","authors":[{"name":"Xiaoying Zhang","slug":"xiaoying-zhang","given":"Xiaoying","family":"Zhang","affiliation":"The Chinese University of Hong Kong"},{"name":"Hong Xie","slug":"hong-xie","given":"Hong","family":"Xie","affiliation":"The Chinese University of Hong Kong"},{"name":"Junzhou Zhao","slug":"junzhou-zhao","given":"Junzhou","family":"Zhao","affiliation":"The Chinese University of Hong Kong"},{"name":"John C.S. Lui","slug":"john-c-s-lui","given":"John C.S.","family":"Lui","affiliation":"The Chinese University of Hong Kong"}],"abstract":"The unbiasedness of online product ratings, an important property to ensure that users’ ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” the distortions from historical ratings in each single rating (or at the micro-level), and perform the “debiasing operations” in real rating systems are the main objectives of this work.\r\nUsing 42 million real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the “Assimilate-Contrast” theory. However, none of the existing works on modeling historical ratings’ influence have taken this into account, and this motivates us to propose the Histori- cal Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users’ real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.","doi":"10.24963/ijcai.2018/763","url":"https://ijcai.org/proceedings/2018/763","pdf":"https://ijcai.org/proceedings/2018/0763.pdf","markdown":"https://ijcai.org/proceedings/2018/763.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/763","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Sister Conferences Best Papers","track":"Best Sister Conferences","number":763,"pages":{"first":5409,"last":5413},"keywords":["Machine Learning: Data Mining","Machine Learning: New Problems"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius et al., 2014] Gediminas Adomavicius, Jesse Bockstedt, Shawn Curley, and Jingjing Zhang. De-biasing user preference ratings in recommender systems. In IntRS Workshop@RecSys 2014, pages 2-9, 2014.","title":"De-biasing user preference ratings in recommender systems","authors":"Gediminas Adomavicius, Jesse Bockstedt, Shawn Curley, Jingjing Zhang","year":"2014","venue":"IntRS Workshop@RecSys 2014","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Adomavicius et al., 2016] Gediminas Adomavicius, Jesse Bockstedt, Shawn P. Curley, and Jingjing Zhang. Under- standing effects of personalized vs. aggregate ratings on user preferences. In IntRS Workshop@RecSys 2016, pages 14-21, 2016.","title":"Understanding effects of personalized vs. aggregate ratings on user preferences","authors":"Gediminas Adomavicius, Jesse Bockstedt, Shawn P Curley, Jingjing Zhang","year":"2016","venue":"IntRS Workshop@RecSys 2016","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Anderson, 1973] Rolph E Anderson. Consumer dissatis- faction: The effect of disconfirmed expectancy on per- ceived product performance. Journal of marketing re- search, pages 38-44, 1973.","title":"Consumer dissatisfaction: The effect of disconfirmed expectancy on perceived product performance","authors":"Rolph E Anderson","year":"1973","venue":"Journal of marketing research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Davidson et al., 2010] James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, et al. The youtube video recommendation system. In Pro- ceedings of the fourth ACM conference on Recommender systems, pages 293-296. ACM, 2010.","title":"The youtube video recommendation system","authors":"James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston","year":"2010","venue":"Proceedings of the fourth ACM conference on Recommender systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Krishnan et al., 2014] Sanjay Krishnan, Jay Patel, Michael Franklin, and Ken Goldberg. Social influence bias in rec- ommender systems: a methodology for learning, analyz- ing, and mitigating bias in ratings. In Proceedings of the 8th ACM Conference on Recommender systems, pages 137-144, 2014.","title":"Social influence bias in recommender systems: a methodology for learning, analyzing, and mitigating bias in ratings","authors":"Sanjay Krishnan, Jay Patel, Michael Franklin, Ken Goldberg","year":"2014","venue":"Proceedings of the 8th ACM Conference on Recommender systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Latané, 1981] Bibb Latané. The psychology of social im- pact. American psychologist, 36(4):343, 1981.","title":"Bibb Latané. The psychology of social impact","authors":"Latané","year":"1981","venue":"American psychologist","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[McAuley et al., 2015] Julian McAuley, Rahul Pandey, and Jure Leskovec. Inferring networks of substitutable and complementary products. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discov- ery and Data Mining, pages 785-794. ACM, 2015.","title":"Inferring networks of substitutable and complementary products","authors":"Julian Mcauley, Rahul Pandey, Jure Leskovec","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Muchnik et al., 2013] Lev Muchnik, Sinan Aral, and Sean J Taylor. Social influence bias: A randomized experiment. Science, 341(6146):647-651, 2013.","title":"Social influence bias: A randomized experiment","authors":"Muchnik","year":"2013","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Oliver, 2014] Richard L Oliver. Satisfaction: A behavioral perspective on the consumer. Routledge, 2014.","title":"Satisfaction: A behavioral perspective on the consumer","authors":"L Richard, Oliver","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ricci et al., 2011] Francesco Ricci, Lior Rokach, and Bracha Shapira. Introduction to recommender systems handbook. Springer, 2011.","title":"Introduction to recommender systems handbook","authors":"Francesco Ricci, Lior Rokach, Bracha Shapira","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Salganik et al., 2006] Matthew J Salganik, Peter Sheridan Dodds, and Duncan J Watts. Experimental study of in- equality and unpredictability in an artificial cultural mar- ket. science, 311(5762):854-856, 2006.","title":"Experimental study of inequality and unpredictability in an artificial cultural market","authors":"J Matthew, Peter Salganik, Duncan J Sheridan Dodds, Watts","year":"2006","venue":"science","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Surowiecki et al., 2007] James Surowiecki, Mark P Silver- man, et al. The wisdom of crowds. American Journal of Physics, 75(2):190-192, 2007.","title":"The wisdom of crowds","authors":"James Surowiecki, Mark P Silverman","year":"2007","venue":"American Journal of Physics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Wang and Zhang, 2013] Jian Wang and Yi Zhang. Opportu- nity models for e-commerce recommendation: Right prod- uct, right time. In SIGIR, 2013.","title":"Opportunity models for e-commerce recommendation: Right product, right time","authors":"Jian Wang, Yi Zhang","year":"2013","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Wang et al., 2014] Ting Wang, Dashun Wang, and Fei Wang. Quantifying herding effects in crowd wisdom. In Proceedings of the 20th ACM SIGKDD international con- ference on Knowledge discovery and data mining, pages 1087-1096. ACM, 2014.","title":"Quantifying herding effects in crowd wisdom","authors":"Ting Wang, Dashun Wang, Fei Wang","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Weninger et al., 2015] Tim Weninger, Thomas James John- ston, and Maria Glenski. Random voting effects in social- digital spaces: A case study of reddit post submissions. In Proceedings of the 26th ACM conference on hypertext & social media, pages 293-297. ACM, 2015.","title":"Random voting effects in socialdigital spaces: A case study of reddit post submissions","authors":"Tim Weninger, Thomas James Johnston, Maria Glenski","year":"2015","venue":"Proceedings of the 26th ACM conference on hypertext & social media","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Yan et al., 2015] Ming Yan, Jitao Sang, and Changsheng Xu. Unified youtube video recommendation via cross- network collaboration. In ICMR, 2015.","title":"Unified youtube video recommendation via crossnetwork collaboration","authors":"Ming Yan, Jitao Sang, Changsheng Xu","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Zhang et al., 2017] Xiaoying Zhang, Junzhou Zhao, and John Lui. Modeling the assimilation-contrast effects in online product rating systems: Debiasing and recommen- dations. In Proceedings of the Eleventh ACM Conference on Recommender Systems, pages 98-106. ACM, 2017.","title":"Modeling the assimilation-contrast effects in online product rating systems: Debiasing and recommendations","authors":"Xiaoying Zhang, Junzhou Zhao, John Lui","year":"2017","venue":"Proceedings of the Eleventh ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}