Input online review data and related bias in recommender systems
Decision Support SystemsPublished 17 February 2012Open access
Selwyn Piramuthu, Gaurav Kapoor, Wei Zhou, Sjouke Mauw
Citations40
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This work considers a specific type of bias that is introduced in online product reviews due to the sequence in which these reviews are written and model several scenarios in this context and study their properties.
Abstract
peer reviewed
Keywords
Computer ScienceDecision Sciences
IEEE Transactions on Knowledge and Data EngineeringToward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
10,115 Citations2005Gediminas Adomavičius, Alexander Tuzhilin
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Decision Support SystemsDo online reviews matter? — An empirical investigation of panel data
1,663 Citations2008Wenjing Duan, Bin Gu +1 more
The result shows that the rating of online user reviews has no significant impact on movies' box office revenues after accounting for the endogeneity, indicating that online user Reviews have little persuasive effect on consumer purchase decisions.
Journal of RetailingThe influence of online product recommendations on consumers’ online choices
1,586 Citations2004Sylvain Sénécal, Jacques Nantel
The online recommendation source labeled “recommender system,” typical of the personalization possibilities offered by online retailing, was more influential than more traditional recommendation sources such as “human experts” and “other consumers”.
SSRN Electronic JournalDo Online Reviews Matter? - an Empirical Investigation of Panel Data
1,032 Citations2005Wenjing Duan, Bin Gu +1 more
The Quarterly Journal of EconomicsFirst Impressions Matter: A Model of Confirmatory Bias
1,006 Citations1999Matthew Rabin, John L. Schrag
Journal of Consumer ResearchAn Attribution Explanation of the Disproportionate Influence of Unfavorable Information
544 Citations1982Richard W. Mizerski
Management ScienceThe Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias
449 Citations2007Chrysanthos Dellarocas, Charles A. Wood
Information Systems ResearchReputation Mechanism Design in Online Trading Environments with Pure Moral Hazard
291 Citations2005Chrysanthos Dellarocas
EBay's simple mechanism is capable of inducing the maximum theoretical efficiency independently of the number of recent ratings that are being summarized in a seller's profile, and optimal policies for dealing with missing feedback and easy online identity changes are derived.
Computers in Human BehaviorHerd behavior in purchasing books online
283 Citations2008Yi‐Fen Chen
Recommendations of other consumers exerted a greater influence on subject choices than recommendations of an expert and recommendations from recommender system influenced online consumer choices more than those from website owners.
IEEE Transactions on Knowledge and Data EngineeringEvaluation and design of online cooperative feedback mechanisms for reputation management
130 Citations2005Ming Fan, Yong Tan +1 more
A new design of reputation system based on exponential smoothing is proposed, shown to be more robust compared to the existing systems and to serve as a sustained incentive mechanism for the seller.
Personality and Social Psychology BulletinNegativity Effects in Impression Formation: A Test in the Political Arena
113 Citations1991Jill G. Klein
IEEE Transactions on Knowledge and Data EngineeringBias and Controversy in Evaluation Systems
27 Citations2008Hady W. Lauw, Ee‐Peng Lim +1 more
This paper proposes a reinforcement-based model that quantifies "evidence," which reveals the degree of confidence with which bias and controversy have been derived and is shown to be effective by experiments on real-life and synthetic data sets.
Does Positivity Bias Explain Patterns of Performance on Wason’s 2-4-6 Task?
11 Citations2019Maggie Gale, Linden J. Ball
Detecting reviewer bias through web-based association mining
6 Citations2008Jessica Staddon, Richard Chow
The method, together with self-regulation, provides for more comprehensive detection of bias in reviews by alerting the user to the potential for an undisclosed relationship between a reviewer and author.
ComplexityCollective increase of first impression bias
3 Citations2009Guillaume Deffuant, Sylvie Huet
