Introduction: We explore the issues that are present in the Netflix Prize dataset. The Netflix Prize seeks to substantially improve the accuracy of user movie rating prediction based on their previous movie preferences and ratings [1]. The contest started in October 2006 and seeks to beat the current Netflix recommendation system by 10% in prediction accuracy. Though some teams have improved pr...

Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories. However, these campaigns are constantly evolving and changing tactics to evade detection. For example, they can launch early attacks on the items to quickly dom...

Online rating systems serve as decision support tool for choosing the right transactions on the internet. Consumers usually rely on others’ experiences when do transaction on the internet, therefore their feedbacks are helpful in succeeding such transactions. One important form of such feedbacks is the product ratings. Most online rating systems have been proposed either by researchers or indus...

Vol. XLIII (August 2006), 355–365 355 © 2006, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) *Yuanping Ying is Assistant Professor of Marketing, School of Management, University of Texas, Dallas (e-mail: [email protected]). Fred Feinberg is Hallman Fellow and Bank One Corporation Associate Professor of Marketing, Stephen M. Ross School of Business, University o...

This research builds upon prior validation studies of the Community of Inquiry (CoI) survey by utilizing multiple ratings measures to validate the survey’s tripartite structure (teaching presence, social presence, & cognitive presence). In prior studies exploring the construct validity of these 3 subscales, only respondents’ course ratings were utilized. This study asked participants to additio...

Collaborative filtering allows the preferences of multiple users to be pooled in a principled way in order to make recommendations about products, services or information unseen by a specific user. We consider here the problem of online and interactive collaborative filtering: given the current ratings and recommendations associated with a user, what queries (new ratings) would most improve the...

Aims: Do college students’ ratings of a professor’s teaching effectiveness suggest that a professor’s teaching improves with time? Does anything predict which instructors receive the highest ratings or improve the fastest? And, importantly, do the correlates of change differ across face-to-face and online courses? Methods: I used data from 10,392 classes taught by 1120 instructors across three ...