Input online review data and related bias in recommender systems

Authors:
Selwyn Piramuthu;Gaurav Kapoor;Wei Zhou;Sjouke Mauw
Affiliations:
Information Systems and Operations Management, University of Florida Gainesville, Florida 32611-7169, USA and RFID European Lab, Paris, France;Information Systems and Operations Management, University of Florida Gainesville, Florida 32611-7169, USA;ESCP Europe, Paris, France and RFID European Lab, Paris, France;Université du Luxembourg, Faculté des Sciences, de la Technologie et de la Communication (FSTC), 6, rue Richard Coudenhove-Kalergi, L-1359, Luxembourg
Venue:
Decision Support Systems
Year:
2012

Citing 10
Cited 4

GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Evaluation and Design of Online Cooperative Feedback Mechanisms for Reputation Management

IEEE Transactions on Knowledge and Data Engineering
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Reputation Mechanism Design in Online Trading Environments with Pure Moral Hazard

Information Systems Research
Herd behavior in purchasing books online

Computers in Human Behavior
Bias and Controversy in Evaluation Systems

IEEE Transactions on Knowledge and Data Engineering
Do online reviews matter? - An empirical investigation of panel data

Decision Support Systems
Detecting reviewer bias through web-based association mining

Proceedings of the 2nd ACM workshop on Information credibility on the web
The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias

Management Science
Collective increase of first impression bias

Complexity

βP: A novel approach to filter out malicious rating profiles from recommender systems

Decision Support Systems
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm

International Journal of Business Information Systems
A hybrid recommendation approach based on attributes of products using genetic algorithm and naive Bayes classifier

International Journal of Business Information Systems
An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product

International Journal of Business Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A majority of extant literature on recommender systems assume the input data as a given to generate recommendations. Both implicit and/or explicit data are used as input in these systems. The existence of various challenges in using such input data including those associated with strategic source manipulations, sparse matrix, state data, among others, are sometimes acknowledged. While such input data are also known to be rife with various forms of bias, to our knowledge no explicit attempt is made to correct or compensate for them in recommender systems. We consider a specific type of bias that is introduced in online product reviews due to the sequence in which these reviews are written. We model several scenarios in this context and study their properties.